[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"_public_publisher_byId_1bc05233-c7d3-44dd-9daf-581d8d48e5bd":3,"_public_publication_all{\"sortAscending\":false,\"sortField\":\"updateTime\",\"page\":0,\"size\":10,\"facet\":true,\"searchKey\":\"publisherId:1bc05233-c7d3-44dd-9daf-581d8d48e5bd,\"}":31},{"code":4,"data":5,"meta":18},"SUCCESS",{"id":6,"createTime":7,"updateTime":8,"relativeEntities":9,"slug":10,"properties":11,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":20,"manageAffiliations":21,"indexDatabases":22,"url":23,"thumbnailPath":18,"statistic":24,"gsStatistic":18,"type":30,"analyzePriority":18},"1bc05233-c7d3-44dd-9daf-581d8d48e5bd","2024-04-11T07:05:18.584+00:00","2024-05-14T18:34:25.032+00:00",[],"Materials-Theory",{"issn":12,"title":14},{"VOID":13},"2509-8012",{"EN":15},"Materials Theory","PUBLISHER","PENDING",null,0,[],[],[],"https:\u002F\u002Flink.springer.com\u002Fjournal\u002F41313",{"impactFactor":19,"impactFactorByYear":25,"i10Index":19,"i10IndexLast5Year":19,"totalPublication":26,"totalPublicationByYear":27,"totalCitation":19,"totalCitationByYear":28,"totalCitationPerPublication":19,"totalCitationPerPublicationByYear":29,"hindexLast5Year":19,"hindex":19},{},1,{"2022":26},{},{},"JOURNAL",{"meta":32,"data":34},{"total":33},"38",[35,152,221,308,421,485,596,686,784,1396],{"id":36,"createTime":37,"updateTime":38,"relativeEntities":39,"slug":40,"properties":41,"entityType":52,"verifyStatus":53,"verifyTime":54,"verifyNote":55,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":56,"fullTextUrl":18,"authors":57,"publicationType":126,"publisherRelationship":127,"citationCount":18,"citationInfo":18,"publishDate":146,"publishYear":147,"citationAnalyzeStatus":148,"lastCitationAnalyze":149,"indexDatabases":150,"openAccess":18,"references":18,"isForceReanalyzing":151},"dc9bdb3a-21c2-4a99-809c-d4df5b877848","2024-02-02T06:44:00.164+00:00","2025-12-11T23:04:55.272+00:00",[],"VQE-method-a-short-survey-and-recent-developments",{"abstract":42,"title":44,"gsPaper":46,"references":48,"doi":50},{"EN":43},"The variational quantum eigensolver (VQE) is a method that uses a hybrid quantum-classical computational approach to find eigenvalues of a Hamiltonian. VQE has been proposed as an alternative to fully quantum algorithms such as quantum phase estimation (QPE) because fully quantum algorithms require quantum hardware that will not be accessible in the near future. VQE has been successfully applied to solve the electronic Schrödinger equation for a variety of small molecules. However, the scalability of this method is limited by two factors: the complexity of the quantum circuits and the complexity of the classical optimization problem. Both of these factors are affected by the choice of the variational ansatz used to represent the trial wave function. Hence, the construction of an efficient ansatz is an active area of research. Put another way, modern quantum computers are not capable of executing deep quantum circuits produced by using currently available ansatzes for problems that map onto more than several qubits. 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Accessed 03 Oct 2021.",{"VOID":51},"10.1186\u002Fs41313-021-00032-6","PUBLICATION","VERIFIED","2024-05-08T07:54:16.936+00:00","Auto Verify","https:\u002F\u002Fmaterialstheory.springeropen.com\u002Farticles\u002F10.1186\u002Fs41313-021-00032-6",[58,83,98,112],{"id":59,"sortIndex":19,"researcher":18,"roles":60,"affiliations":62,"properties":80,"displayName":82,"givenName":18,"familyName":18},"e28766f2-f138-4426-893b-de96e9e86a71",[61],"AUTHOR",[63,71],{"id":64,"sortIndex":19,"affiliation":65,"properties":18},"a5df1f7a-cc78-47bf-8022-3a99cb1796f9",{"id":64,"createTime":18,"updateTime":18,"relativeEntities":66,"slug":18,"properties":67,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":70,"statistic":18},[],{"title":68},{"EN":69},"[Oak Ridge Associated Universities, Oak Ridge, USA]",[],{"id":72,"sortIndex":26,"affiliation":73,"properties":79},"e27ec173-69a7-4ced-b828-0e4079a31a6b",{"id":72,"createTime":18,"updateTime":18,"relativeEntities":74,"slug":18,"properties":75,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":78,"statistic":18},[],{"title":76},{"VI":77},"Computational Science Division, Argonne National Laboratory, Lemont, USA",[],{},{"title":81},{"VI":82},"Dmitry A. Fedorov",{"id":84,"sortIndex":26,"researcher":18,"roles":85,"affiliations":86,"properties":95,"displayName":97,"givenName":18,"familyName":18},"f61b38bf-80d5-46dd-a082-cfdd3959c272",[61],[87],{"id":88,"sortIndex":19,"affiliation":89,"properties":18},"d28e5163-ac59-4936-805e-223d4ccae97d",{"id":88,"createTime":18,"updateTime":18,"relativeEntities":90,"slug":18,"properties":91,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":94,"statistic":18},[],{"title":92},{"VI":93},"Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, USA",[],{"title":96},{"VI":97},"Bo Peng",{"id":99,"sortIndex":100,"researcher":18,"roles":101,"affiliations":102,"properties":109,"displayName":111,"givenName":18,"familyName":18},"a1b6cbf8-5987-4ae6-b6fa-7f907f5098e5",2,[61],[103],{"id":88,"sortIndex":19,"affiliation":104,"properties":18},{"id":88,"createTime":18,"updateTime":18,"relativeEntities":105,"slug":18,"properties":106,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":108,"statistic":18},[],{"title":107},{"VI":93},[],{"title":110},{"VI":111},"Niranjan Govind",{"id":113,"sortIndex":114,"researcher":18,"roles":115,"affiliations":116,"properties":123,"displayName":125,"givenName":18,"familyName":18},"fe1baedb-0c13-4770-9f19-2f0db9c2a65c",3,[61],[117],{"id":72,"sortIndex":19,"affiliation":118,"properties":18},{"id":72,"createTime":18,"updateTime":18,"relativeEntities":119,"slug":18,"properties":120,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":122,"statistic":18},[],{"title":121},{"VI":77},[],{"title":124},{"VI":125},"Yuri Alexeev","ARTICLE",{"url":56,"publisher":128,"properties":141},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":129,"slug":10,"properties":130,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":133,"manageAffiliations":134,"indexDatabases":135,"url":23,"thumbnailPath":18,"statistic":136,"gsStatistic":18,"type":30,"analyzePriority":18},[],{"issn":131,"title":132},{"VOID":13},{"EN":15},[],[],[],{"impactFactor":19,"impactFactorByYear":137,"i10Index":19,"i10IndexLast5Year":19,"totalPublication":26,"totalPublicationByYear":138,"totalCitation":19,"totalCitationByYear":139,"totalCitationPerPublication":19,"totalCitationPerPublicationByYear":140,"hindexLast5Year":19,"hindex":19},{},{"2022":26},{},{},{"pages":142,"volume":144},{"VOID":143},"1-21",{"VOID":145},"6","2022-01-06",2022,"ERROR_IN_GET_PLATFORM_ID","2025-12-11T23:04:55.271+00:00",[],false,{"id":153,"createTime":154,"updateTime":155,"relativeEntities":156,"slug":157,"properties":158,"entityType":52,"verifyStatus":53,"verifyTime":155,"verifyNote":55,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":167,"fullTextUrl":18,"authors":168,"publicationType":126,"publisherRelationship":199,"citationCount":18,"citationInfo":18,"publishDate":218,"publishYear":219,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":220,"openAccess":18,"references":18,"isForceReanalyzing":151},"31a0e4b1-239a-486b-b47d-c604847bf03c","2024-02-06T17:31:32.970+00:00","2025-02-25T15:38:16.332+00:00",[],"Cell-structure-formation-in-a-two-dimensional-density-based-dislocation-dynamics-model",{"abstract":159,"title":161,"references":163,"doi":165},{"EN":160},"Cellular patterns formed by self-organization of dislocations are a most conspicuous feature of dislocation microstructure evolution during plastic deformation. To elucidate the physical mechanisms underlying dislocation cell structure formation, we use a minimal model for the evolution of dislocation densities under load. By considering only two slip systems in a plane strain setting, we arrive at a model which is amenable to analytical stability analysis and numerical simulation. We use this model to establish analytical stability criteria for cell structures to emerge, to investigate the dynamics of the patterning process and establish the mechanism of pattern wavelength selection. This analysis demonstrates an intimate relationship between hardening and cell structure formation, which appears as an almost inevitable corollary to dislocation dominated strain hardening. Specific mechanisms such as cross slip, by contrast, turn out to be incidental to the formation of cellular patterns.",{"EN":162},"Cell structure formation in a two-dimensional density-based dislocation dynamics model",{"VOID":164},"Y. Aoyagi, R. Kobayashi, Y. Kaji, K. 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Kubin, Scaling laws for dislocation microstructures in monotonic and cyclic deformation of fcc metals. Prog. Mater. Sci.56(6), 725–784 (2011).\nJ. Schwerdtfeger, E. Nadgorny, V. Koutsos, J. R. Blackford, M. Zaiser, Statistical heterogeneity of plastic deformation: An investigation based on surface profilometry. Acta Mater.58:, 4859–4870 (2010).\nG. Streb, B. Reppich, Steady state deformation and dislocation structure of pure and Mg-doped LiF single crystals. II. Etch pit studies of dislocation structure. Phys. Status Solidi A. 16:, 493–505 (1973).\nF. Szekely, I. Groma, J. Lendvai, Characterization of self-similar dislocation structures by X-ray diffraction. Mater. Sci. Eng. A. 324(1-2), 179–182 (2002).\nP. L. Valdenaire, Y. Le Bouar, B. Appolaire, A. Finel, Density-based crystal plasticity: From the discrete to the continuum. Phys. Rev. B. 93:, 214111 (2016).\nD. Walgraef, E. C. Aifantis, Dislocation patterning in fatigued metals as a result of dynamical instabilities. J. Appl. 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Zaiser, Local density approximation for the energy functional of three-dimensional dislocation systems. Phys. Rev. B. 92(17), 174120 (2015).\nM. Zaiser, P. Moretti, Fluctuation phenomena in crystal plasticity–a continuum model. J. Stat. Mech. Theory Exp.2005(08), 08004 (2005).\nM. Zaiser, S. Sandfeld, Scaling properties of dislocation simulations in the similitude regime. Model. Simul. Mater. Sci. Eng.22:, 065012 (2014).",{"VOID":166},"10.1186\u002Fs41313-020-00025-x","https:\u002F\u002Fmaterialstheory.springeropen.com\u002Farticles\u002F10.1186\u002Fs41313-020-00025-x",[169,184],{"id":170,"sortIndex":19,"researcher":18,"roles":171,"affiliations":172,"properties":181,"displayName":183,"givenName":18,"familyName":18},"f1ad366c-e47d-4f33-8b90-76fec5bdf69d",[61],[173],{"id":174,"sortIndex":19,"affiliation":175,"properties":18},"316403e5-0bcc-47b0-8117-6ab70d488182",{"id":174,"createTime":18,"updateTime":18,"relativeEntities":176,"slug":18,"properties":177,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":180,"statistic":18},[],{"title":178},{"VI":179},"School of Mechanics, Civil Engineering and Architecture, Northwestern Polytechnical University, Xian, P.R. China",[],{"title":182},{"VI":183},"Ronghai Wu",{"id":185,"sortIndex":26,"researcher":18,"roles":186,"affiliations":187,"properties":196,"displayName":198,"givenName":18,"familyName":18},"3a167984-f44d-4d60-b734-6769c73c5dfb",[61],[188],{"id":189,"sortIndex":19,"affiliation":190,"properties":18},"fe4c1b3e-36b4-413f-803c-3cdbaeb67819",{"id":189,"createTime":18,"updateTime":18,"relativeEntities":191,"slug":18,"properties":192,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":195,"statistic":18},[],{"title":193},{"VI":194},"Department of Materials Science, WW8-Materials Simulation, Friedrich-Alexander Universität Erlangen-Nürnberg, Fürth, Germany",[],{"title":197},{"VI":198},"Michael Zaiser",{"url":167,"publisher":200,"properties":213},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":201,"slug":10,"properties":202,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":205,"manageAffiliations":206,"indexDatabases":207,"url":23,"thumbnailPath":18,"statistic":208,"gsStatistic":18,"type":30,"analyzePriority":18},[],{"issn":203,"title":204},{"VOID":13},{"EN":15},[],[],[],{"impactFactor":19,"impactFactorByYear":209,"i10Index":19,"i10IndexLast5Year":19,"totalPublication":26,"totalPublicationByYear":210,"totalCitation":19,"totalCitationByYear":211,"totalCitationPerPublication":19,"totalCitationPerPublicationByYear":212,"hindexLast5Year":19,"hindex":19},{},{"2022":26},{},{},{"pages":214,"volume":216},{"VOID":215},"1-22",{"VOID":217},"5","2021-05-04",2021,[],{"id":222,"createTime":223,"updateTime":224,"relativeEntities":225,"slug":226,"properties":227,"entityType":52,"verifyStatus":53,"verifyTime":224,"verifyNote":55,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":238,"fullTextUrl":18,"authors":239,"publicationType":126,"publisherRelationship":291,"citationCount":18,"citationInfo":18,"publishDate":305,"publishYear":306,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":307,"openAccess":18,"references":18,"isForceReanalyzing":151},"f5d84f7b-0554-41cd-b465-311935286d10","2024-04-08T20:26:15.761+00:00","2025-02-25T12:47:03.686+00:00",[],"Probing-the-transition-from-dislocation-jamming-to-pinning-by-machine-learning",{"abstract":228,"title":230,"keywords":232,"references":234,"doi":236},{"EN":229},"Collective motion of dislocations is governed by the obstacles they encounter. In pure crystals, dislocations form complex structures as they become jammed by their anisotropic shear stress fields. On the other hand, introducing disorder to the crystal causes dislocations to pin to these impeding elements and, thus, leads to a competition between dislocation-dislocation and dislocation-disorder interactions. Previous studies have shown that, depending on the dominating interaction, the mechanical response and the way the crystal yields change.Here we employ three-dimensional discrete dislocation dynamics simulations with varying density of fully coherent precipitates to study this phase transition − from jamming to pinning − using unsupervised machine learning. By constructing descriptors characterizing the evolving dislocation configurations during constant loading, a confusion algorithm is shown to be able to distinguish the systems into two separate phases. These phases agree well with the observed changes in the relaxation rate during the loading. Our results also give insights on the structure of the dislocation networks in the two phases.",{"EN":231},"Probing the transition from dislocation jamming to pinning by machine learning",{"EN":233},"",{"VOID":235},"A. Ardell, Precipitation hardening. Metall. Trans. A. 16(12), 2131–2165 (1985).\nA. Arsenlis, W. Cai, M. Tang, M. Rhee, T. Oppelstrup, G. Hommes, T. G. Pierce, V. V. Bulatov, Enabling strain hardening simulations with dislocation dynamics. Model. Simul. Mater. Sci. Eng.15(6), 553 (2007).\nA. Arsenlis, D. Parks, Crystallographic aspects of geometrically-necessary and statistically-stored dislocation density. Acta Mater.47(5), 1597–1611 (1999).\nC. M. Bishop, Pattern recognition and machine learning (Springer, New York, 2006).\nV. V. Bulatov, M. Rhee, W. Cai, Periodic boundary conditions for dislocation dynamics simulations in three dimensions. MRS Online Proc. Libr. Arch.653:, Z1.3 (2000).\nJ. Carrasquilla, R. G. Melko, Machine learning phases of matter. Nat. 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B. 99(4), 041108 (2019).\nR. B. Sills, N. Bertin, A. Aghaei, W. Cai, Dislocation networks and the microstructural origin of strain hardening. Phys. Rep. Lett.121(8), 085501 (2018).\nG. Sparks, R. Maaß, Nontrivial scaling exponents of dislocation avalanches in microplasticity. Phys. Rev. Mater.2(12), 120601 (2018).\nD. Steinberger, H. Song, S. Sandfeld, Machine learning-based classification of dislocation microstructures. Front. Mater.6:, 141 (2019).\nE. P. Van Nieuwenburg, Y. -H. Liu, S. D. Huber, Learning phase transitions by confusion. Nat. Phys.13(5), 435 (2017).\nZ. Yang, S. Papanikolaou, A. C. Reid, W. -k. Liao, A. N. Choudhary, C. Campbell, A. Agrawal, Learning to predict crystal plasticity at the nanoscale: Deep residual networks and size effects in uniaxial compression discrete dislocation simulations. Sci. Rep.10(1), 1–14 (2020).\nM. Zaiser, Scale invariance in plastic flow of crystalline solids. Adv. Phys.55:, 185–245 (2006).\nL. Zdeborová, Machine learning: New tool in the box. Nat. Phys.13(5), 420 (2017).\nY. Zhang, A. H. Ngan, Extracting dislocation microstructures by deep learning. Int. J. Plast.115:, 18–28 (2019).\nP. Zhang, O. U. Salman, J. -Y. Zhang, G. Liu, J. Weiss, L. Truskinovsky, J. Sun, Taming intermittent plasticity at small scales. Acta Mater.128:, 351–364 (2017).",{"VOID":237},"10.1186\u002Fs41313-020-00022-0","https:\u002F\u002Fmaterialstheory.springeropen.com\u002Farticles\u002F10.1186\u002Fs41313-020-00022-0",[240,255,270],{"id":241,"sortIndex":19,"researcher":18,"roles":242,"affiliations":243,"properties":252,"displayName":254,"givenName":18,"familyName":18},"08497bf7-e73d-4196-97dd-f60e5c6b4584",[61],[244],{"id":245,"sortIndex":19,"affiliation":246,"properties":18},"8e250e16-3d7f-476b-9963-1ea0c0d79d7f",{"id":245,"createTime":18,"updateTime":18,"relativeEntities":247,"slug":18,"properties":248,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":251,"statistic":18},[],{"title":249},{"VI":250},"Aalto University, Department of Applied Physics, Espoo, Finland",[],{"title":253},{"VI":254},"Henri Salmenjoki",{"id":256,"sortIndex":26,"researcher":18,"roles":257,"affiliations":258,"properties":267,"displayName":269,"givenName":18,"familyName":18},"500c589c-a636-47b0-b61a-30e1b9d70360",[61],[259],{"id":260,"sortIndex":19,"affiliation":261,"properties":18},"0e2b7400-41a0-4532-8996-7047f3f59ea5",{"id":260,"createTime":18,"updateTime":18,"relativeEntities":262,"slug":18,"properties":263,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":266,"statistic":18},[],{"title":264},{"VI":265},"Computational Physics Laboratory, Tampere University, Tampere, Finland",[],{"title":268},{"VI":269},"Lasse Laurson",{"id":271,"sortIndex":100,"researcher":18,"roles":272,"affiliations":273,"properties":288,"displayName":290,"givenName":18,"familyName":18},"a066a0e2-2ae9-4fa0-a965-ddd94d9d989c",[61],[274,280],{"id":245,"sortIndex":19,"affiliation":275,"properties":18},{"id":245,"createTime":18,"updateTime":18,"relativeEntities":276,"slug":18,"properties":277,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":279,"statistic":18},[],{"title":278},{"VI":250},[],{"id":281,"sortIndex":19,"affiliation":282,"properties":18},"12b7deca-8352-444f-8251-ecde04dab2ab",{"id":281,"createTime":18,"updateTime":18,"relativeEntities":283,"slug":18,"properties":284,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":287,"statistic":18},[],{"title":285},{"VI":286},"NOMATEN Centre of Excellence, National Centre for Nuclear Research, Otwock-Swierk, Poland",[],{"title":289},{"VI":290},"Mikko J. Alava",{"url":18,"publisher":292,"properties":18},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":293,"slug":10,"properties":294,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":297,"manageAffiliations":298,"indexDatabases":299,"url":23,"thumbnailPath":18,"statistic":300,"gsStatistic":18,"type":30,"analyzePriority":18},[],{"issn":295,"title":296},{"VOID":13},{"EN":15},[],[],[],{"impactFactor":19,"impactFactorByYear":301,"i10Index":19,"i10IndexLast5Year":19,"totalPublication":26,"totalPublicationByYear":302,"totalCitation":19,"totalCitationByYear":303,"totalCitationPerPublication":19,"totalCitationPerPublicationByYear":304,"hindexLast5Year":19,"hindex":19},{},{"2022":26},{},{},"2020-10-09",2020,[],{"id":309,"createTime":310,"updateTime":311,"relativeEntities":312,"slug":313,"properties":314,"entityType":52,"verifyStatus":53,"verifyTime":311,"verifyNote":55,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":325,"fullTextUrl":18,"authors":326,"publicationType":126,"publisherRelationship":401,"citationCount":18,"citationInfo":18,"publishDate":419,"publishYear":219,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":420,"openAccess":18,"references":18,"isForceReanalyzing":151},"f293e00e-ba18-4309-b338-bd69d23a74b2","2023-11-05T09:13:05.876+00:00","2025-02-23T11:15:40.882+00:00",[],"Slip-free-multiplication-and-complexity-of-dislocation-networks-in-FCC-metals",{"abstract":315,"title":317,"keywords":319,"references":321,"doi":323},{"EN":316},"During plastic deformation of crystalline solids, intricate networks of dislocation lines form and evolve. To capture dislocation density evolution, prominent theories of crystal plasticity assume that 1) multiplication is driven by slip in active slip systems and 2) pair-wise slip system interactions dominate network evolution. In this work, we analyze a massive database of over 100 discrete dislocation dynamics simulations (with cross-slip suppressed), and our findings bring both of these assumptions into question. We demonstrate that dislocation multiplication is commonly observed on slip systems with no applied stress and no plastic strain rate, a phenomenon we refer to as slip-free multiplication. We show that while the formation of glissile junctions provides one mechanism for slip-free multiplication, additional mechanisms which account for the influence of coplanar interactions are needed to fully explain the observations. Unlike glissile junction formation which results from a binary reaction between a pair of slip systems, these new multiplication mechanisms require higher order reactions that lead to complex network configurations. While these complex configurations have not been given much attention previously, they account for about 50% of the line intersections in our database.",{"EN":318},"Slip-free multiplication and complexity of dislocation networks in FCC metals",{"EN":320},"Characterization and Evaluation of Materials,Condensed Matter Physics,Physical Chemistry,Materials Engineering",{"VOID":322},"S. Akhondzadeh, R. B. Sills, N. Bertin, W. Cai, Dislocation density-based plasticity model from massive discrete dislocation dynamics database. J. Mech. Phys. Solids. 145:, 104152 (2020).\nP. M. Anderson, J. P. Hirth, J. Lothe, Theory of dislocations, 2017th edn. (Cambridge University Press, Cambridge, 2017).\nA. S. Argon, Strengthening mechanisms in crystal plasticity. 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Acta Mater.58(4), 1152–1211 (2010). https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.actamat.2009.10.058.\nR. B. Sills, A. Aghaei, W. Cai, Advanced time integration algorithms for dislocation dynamics simulations of work hardening. Model. Simul. Mater. Sci. Eng.24(4), 045019 (2016).\nR. B. Sills, N. Bertin, A. Aghaei, W. Cai, Dislocation networks and the microstructural origin of strain hardening. Phys. Rev. Lett.121(8), 085501 (2018).\nM. Stricker, D. Weygand, Dislocation multiplication mechanisms–glissile junctions and their role on the plastic deformation at the microscale. Acta Mater.99:, 130–139 (2015).\nM. Sudmanns, M. Stricker, D. Weygand, T. Hochrainer, K. Schulz, Dislocation multiplication by cross-slip and glissile reaction in a dislocation based continuum formulation of crystal plasticity. J. Mech. Phys. Solids. 132:, 103695 (2019). https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jmps.2019.103695.\nD. Weygand, Mechanics and dislocation structures at the micro-scale: Insights on dislocation multiplication mechanisms from discrete dislocation dynamics simulations. MRS Proceedings, 1651, Mrsf13-1651-kk07-02. (2014). https:\u002F\u002Fdoi.org\u002F10.1557\u002Fopl.2014.362.\nL. A. Zepeda-Ruiz, A. Stukowski, T. Oppelstrup, N. Bertin, N. R. Barton, R. Freitas, V. V. Bulatov, Atomistic insights into metal hardening. Nat. Mater.1–6 (2020). Nature Publishing Group.",{"VOID":324},"10.1186\u002Fs41313-020-00024-y","https:\u002F\u002Fmaterialstheory.springeropen.com\u002Farticles\u002F10.1186\u002Fs41313-020-00024-y",[327,356,371,386],{"id":328,"sortIndex":19,"researcher":18,"roles":329,"affiliations":330,"properties":353,"displayName":355,"givenName":18,"familyName":18},"88ed998e-beb4-476f-8225-bd2d4826b74e",[61],[331,342],{"id":332,"sortIndex":19,"affiliation":333,"properties":339},"6ce96771-dee3-4a8f-a559-ec0872b6d262",{"id":332,"createTime":18,"updateTime":18,"relativeEntities":334,"slug":18,"properties":335,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":338,"statistic":18},[],{"title":336},{"EN":337},"Department of Mechanical Engineering, Stanford University, Stanford, United States;",[],{"title":340},{"VI":341},"Department of Mechanical Engineering, Stanford University, Stanford, USA",{"id":343,"sortIndex":19,"affiliation":344,"properties":350},"ca339050-7b48-451e-b7ce-744dee3af5fb",{"id":343,"createTime":18,"updateTime":18,"relativeEntities":345,"slug":18,"properties":346,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":349,"statistic":18},[],{"title":347},{"EN":348},"Lawrence Livermore National Laboratory, Livermore, United States",[],{"title":351},{"VI":352},"Lawrence Livermore National Laboratory, Livermore, USA",{"title":354},{"VI":355},"Nicolas Bertin",{"id":357,"sortIndex":19,"researcher":18,"roles":358,"affiliations":359,"properties":368,"displayName":370,"givenName":18,"familyName":18},"0adf75f8-1083-46b9-a5a7-6ec60ba10d39",[61],[360],{"id":361,"sortIndex":19,"affiliation":362,"properties":18},"4b77fab9-8b8a-4061-ba48-71bda1e6878d",{"id":361,"createTime":18,"updateTime":18,"relativeEntities":363,"slug":18,"properties":364,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":367,"statistic":18},[],{"title":365},{"VI":366},"Department of Materials Science and Engineering, Rutgers University, Piscataway, USA",[],{"title":369},{"VI":370},"Ryan B. Sills",{"id":372,"sortIndex":19,"researcher":18,"roles":373,"affiliations":374,"properties":383,"displayName":385,"givenName":18,"familyName":18},"b02918b9-540f-40bd-be65-ce9453af432a",[61],[375],{"id":332,"sortIndex":19,"affiliation":376,"properties":381},{"id":332,"createTime":18,"updateTime":18,"relativeEntities":377,"slug":18,"properties":378,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":380,"statistic":18},[],{"title":379},{"EN":337},[],{"title":382},{"VI":341},{"title":384},{"VI":385},"Wei Cai",{"id":387,"sortIndex":19,"researcher":18,"roles":388,"affiliations":389,"properties":398,"displayName":400,"givenName":18,"familyName":18},"f08c222f-e272-495c-8c08-1cc4713fad1d",[61],[390],{"id":332,"sortIndex":19,"affiliation":391,"properties":396},{"id":332,"createTime":18,"updateTime":18,"relativeEntities":392,"slug":18,"properties":393,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":395,"statistic":18},[],{"title":394},{"EN":337},[],{"title":397},{"VI":341},{"title":399},{"VI":400},"Sh. Akhondzadeh",{"url":325,"publisher":402,"properties":415},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":403,"slug":10,"properties":404,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":407,"manageAffiliations":408,"indexDatabases":409,"url":23,"thumbnailPath":18,"statistic":410,"gsStatistic":18,"type":30,"analyzePriority":18},[],{"issn":405,"title":406},{"VOID":13},{"EN":15},[],[],[],{"impactFactor":19,"impactFactorByYear":411,"i10Index":19,"i10IndexLast5Year":19,"totalPublication":26,"totalPublicationByYear":412,"totalCitation":19,"totalCitationByYear":413,"totalCitationPerPublication":19,"totalCitationPerPublicationByYear":414,"hindexLast5Year":19,"hindex":19},{},{"2022":26},{},{},{"pages":416,"volume":418},{"VOID":417},"1-24",{"VOID":217},"2021-03-29",[],{"id":422,"createTime":423,"updateTime":424,"relativeEntities":425,"slug":426,"properties":427,"entityType":52,"verifyStatus":53,"verifyTime":424,"verifyNote":55,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":436,"fullTextUrl":18,"authors":437,"publicationType":126,"publisherRelationship":466,"citationCount":18,"citationInfo":18,"publishDate":146,"publishYear":147,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":484,"openAccess":18,"references":18,"isForceReanalyzing":151},"b54d5f82-5d27-4dfb-b055-fcf5b35d9c43","2024-02-05T12:12:35.354+00:00","2025-02-18T16:14:50.003+00:00",[],"Pinning-of-dislocations-in-disordered-alloys-effects-of-dislocation-orientation",{"abstract":428,"title":430,"references":432,"doi":434},{"EN":429},"The current interest in compositionally complex alloys including so called high entropy alloys has caused renewed interest in the general problem of solute hardening. It has been suggested that this problem can be addressed by treating the alloy as an effective medium containing a random distribution of dilatation and compression centers representing the volumetric misfit of atoms of different species. The mean square stresses arising from such a random distribution can be calculated analytically, their spatial correlations are strongly anisotropic and exhibit long-range tails with third-order power law decay (Geslin and Rodney 2021; Geslin et al. 2021). Here we discuss implications of the anisotropic and long-range nature of the correlation functions for the pinning of dislocations of arbitrary orientation. While edge dislocations are found to follow the standard pinning paradigm, for dislocations of near screw orientation we demonstrate the co-existence of two types of pinning energy minima.",{"EN":431},"Pinning of dislocations in disordered alloys: effects of dislocation orientation",{"VOID":433},"B. Bakó, D. Weygand, M. Samaras, W. Hoffelner, M. Zaiser, Dislocation depinning transition in a dispersion-strengthened steel. Phys. Rev. B. 78(14), 144104 (2008).\nP. Chauve, T. Giamarchi, P. Le Doussal, Creep and depinning in disordered media. Phys. Rev. B. 62(10), 6241 (2000).\nS. F. Edwards, D. Wilkinson, The surface statistics of a granular aggregate. Proc. R. Soc. Lond. A. Math. Phys. Sci.381(1780), 17–31 (1982).\nP. -A. Geslin, D. Rodney, Microelasticity model of random alloys. part i: mean square displacements and stresses. J. Mech. Phys. Solids. 153:, 104479 (2021).\nP. -A. Geslin, A. Rida, D. Rodney, Microelasticity model of random alloys. part ii: displacement and stress correlations. J. Mech. Phys. Solids. 153:, 104480 (2021).\nL. B. Ioffe, V. M. Vinokur, Dynamics of interfaces and dislocations in disordered media. J. Phys. C Solid State Phys.20(36), 6149 (1987).\nR. Kubilay, A. Ghafarollahi, F. Maresca, W. Curtin, High energy barriers for edge dislocation motion in body-centered cubic high entropy alloys. 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Bei, Thermal activation mechanisms and labusch-type strengthening analysis for a family of high-entropy and equiatomic solid-solution alloys. Acta Mater.120:, 108–119 (2016).\nM. Zaiser, Dislocation motion in a random solid solution. Phil. Mag. A. 82(15), 2869–2883 (2002).\nS. Zapperi, M. Zaiser, Depinning of a dislocation: the influence of long-range interactions. Mater. Sci. Eng. A. 309(2), 348–351 (2001).\nJ. -H. Zhai, M. Zaiser, Properties of dislocation lines in crystals with strong atomic-scale disorder. Mater. Sci. Eng. A. 740:, 285–294 (2019).",{"VOID":435},"10.1186\u002Fs41313-021-00036-2","https:\u002F\u002Fmaterialstheory.springeropen.com\u002Farticles\u002F10.1186\u002Fs41313-021-00036-2",[438,452],{"id":439,"sortIndex":19,"researcher":18,"roles":440,"affiliations":441,"properties":450,"displayName":198,"givenName":18,"familyName":18},"e97b3abf-619d-4cd0-a08b-85341b3a5a50",[61],[442],{"id":443,"sortIndex":19,"affiliation":444,"properties":18},"380c94cd-50e5-406b-bb61-b8876019a9f4",{"id":443,"createTime":18,"updateTime":18,"relativeEntities":445,"slug":18,"properties":446,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":449,"statistic":18},[],{"title":447},{"VI":448},"Department of Materials Simulation, WW8-Materials Simulation, Friedrich-Alexander Universität Erlangen-Nürnberg, Fürth, Germany",[],{"title":451},{"VI":198},{"id":453,"sortIndex":26,"researcher":18,"roles":454,"affiliations":455,"properties":464,"displayName":183,"givenName":18,"familyName":18},"636a67a0-1758-4304-9c9a-e7a1b845d9b6",[61],[456],{"id":457,"sortIndex":19,"affiliation":458,"properties":18},"4a35fe50-b018-4411-9823-46a06842c2b6",{"id":457,"createTime":18,"updateTime":18,"relativeEntities":459,"slug":18,"properties":460,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":463,"statistic":18},[],{"title":461},{"VI":462},"School of Mechanics, Civil Engineering and Architecture, Northwestern Polytechnical University, Xian, People’s Republic of China",[],{"title":465},{"VI":183},{"url":436,"publisher":467,"properties":480},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":468,"slug":10,"properties":469,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":472,"manageAffiliations":473,"indexDatabases":474,"url":23,"thumbnailPath":18,"statistic":475,"gsStatistic":18,"type":30,"analyzePriority":18},[],{"issn":470,"title":471},{"VOID":13},{"EN":15},[],[],[],{"impactFactor":19,"impactFactorByYear":476,"i10Index":19,"i10IndexLast5Year":19,"totalPublication":26,"totalPublicationByYear":477,"totalCitation":19,"totalCitationByYear":478,"totalCitationPerPublication":19,"totalCitationPerPublicationByYear":479,"hindexLast5Year":19,"hindex":19},{},{"2022":26},{},{},{"pages":481,"volume":483},{"VOID":482},"1-13",{"VOID":145},[],{"id":486,"createTime":487,"updateTime":488,"relativeEntities":489,"slug":490,"properties":491,"entityType":52,"verifyStatus":53,"verifyTime":488,"verifyNote":55,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":501,"fullTextUrl":18,"authors":502,"publicationType":126,"publisherRelationship":579,"citationCount":18,"citationInfo":18,"publishDate":593,"publishYear":594,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":595,"openAccess":18,"references":18,"isForceReanalyzing":151},"70ca5a4d-6aa6-4ae8-aedd-5b0d85cf1c44","2024-04-07T16:06:33.263+00:00","2025-02-16T07:34:40.256+00:00",[],"Overdamped-langevin-dynamics-simulations-of-grain-boundary-motion",{"abstract":492,"title":494,"keywords":496,"references":497,"doi":499},{"EN":493},"Macroscopic properties of structural materials are strongly dependent on their microstructure. However, the modeling of their evolution is a complex task because of the mechanisms involved such as plasticity, recrystallization, and phase transformations, which are common processes taking place in metallic alloys. This complexity led to a growing interest in atomistic simulations formulated without any auxiliary hypotheses beyond the choice of interatomic potential. In this context, we propose here a model based on an overdamped stochastic evolution of particles interacting through inter-atomic forces. The model settles to the correct thermal equilibrium distribution in canonical and grand-canonical ensembles and is used to study the grain boundary migration. Finally, a comparison of our results with those obtained by molecular dynamics shows that our approach reproduces the complex atomic-scale dynamics of grain boundary migration correctly.",{"EN":495},"Overdamped langevin dynamics simulations of grain boundary motion",{"EN":233},{"VOID":498},"T. Ando, T. Meguro, I. 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Voorhees, Phase field crystal simulations of nanocrystalline grain growth in two dimensions. Acta Mater.60(1), 407–419 (2012).\nS. Yip, Handbook of Materials Modeling, vol. 1 (Springer, Netherlands, 2005).\nP. Zhang, O. U. Salman, J. -Y. Zhang, G. Liu, J. Weiss, L. Truskinovsky, J. Sun, Taming intermittent plasticity at small scales. Acta Mater.128:, 351–364 (2017).\nL. A. Zepeda-Ruiz, A. Stukowski, T. Oppelstrup, V. V. Bulatov, Probing the limits of metal plasticity with molecular dynamics simulations. Nature. 550:, 492 (2017).",{"VOID":500},"10.1186\u002Fs41313-019-0016-1","https:\u002F\u002Fmaterialstheory.springeropen.com\u002Farticles\u002F10.1186\u002Fs41313-019-0016-1",[503,526,539,552,565],{"id":504,"sortIndex":19,"researcher":18,"roles":505,"affiliations":506,"properties":523,"displayName":525,"givenName":18,"familyName":18},"307ecd5f-e5e5-4f2c-8e4e-abdcc44bbffe",[61],[507,515],{"id":508,"sortIndex":19,"affiliation":509,"properties":18},"921c931b-5ce1-4433-8fad-ef4a92b61e48",{"id":508,"createTime":18,"updateTime":18,"relativeEntities":510,"slug":18,"properties":511,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":514,"statistic":18},[],{"title":512},{"VI":513},"Laboratoire d’Etude des Microstructures, ONERA, CNRS, Université Paris-Saclay, Châtillon, France",[],{"id":516,"sortIndex":19,"affiliation":517,"properties":18},"5854b207-09fb-490e-83b6-9693c510946f",{"id":516,"createTime":18,"updateTime":18,"relativeEntities":518,"slug":18,"properties":519,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":522,"statistic":18},[],{"title":520},{"VI":521},"CNRS, LSPM UPR3407, Université Paris 13, Sorbonne Paris Cité, Villetaneuse, France",[],{"title":524},{"VI":525},"Carolina Baruffi",{"id":527,"sortIndex":26,"researcher":18,"roles":528,"affiliations":529,"properties":536,"displayName":538,"givenName":18,"familyName":18},"fac58d85-b529-462d-a4c0-9fbcd7aea752",[61],[530],{"id":508,"sortIndex":19,"affiliation":531,"properties":18},{"id":508,"createTime":18,"updateTime":18,"relativeEntities":532,"slug":18,"properties":533,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":535,"statistic":18},[],{"title":534},{"VI":513},[],{"title":537},{"VI":538},"Alphonse Finel",{"id":540,"sortIndex":100,"researcher":18,"roles":541,"affiliations":542,"properties":549,"displayName":551,"givenName":18,"familyName":18},"74c57d08-69d1-4bdc-8979-a91efcc5a52f",[61],[543],{"id":508,"sortIndex":19,"affiliation":544,"properties":18},{"id":508,"createTime":18,"updateTime":18,"relativeEntities":545,"slug":18,"properties":546,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":548,"statistic":18},[],{"title":547},{"VI":513},[],{"title":550},{"VI":551},"Yann Le Bouar",{"id":553,"sortIndex":114,"researcher":18,"roles":554,"affiliations":555,"properties":562,"displayName":564,"givenName":18,"familyName":18},"ac735e46-da5c-498c-b624-45366277035d",[61],[556],{"id":516,"sortIndex":19,"affiliation":557,"properties":18},{"id":516,"createTime":18,"updateTime":18,"relativeEntities":558,"slug":18,"properties":559,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":561,"statistic":18},[],{"title":560},{"VI":521},[],{"title":563},{"VI":564},"Brigitte Bacroix",{"id":566,"sortIndex":567,"researcher":18,"roles":568,"affiliations":569,"properties":576,"displayName":578,"givenName":18,"familyName":18},"310c1ea1-77e3-42d1-8599-7c071fcde08a",4,[61],[570],{"id":516,"sortIndex":19,"affiliation":571,"properties":18},{"id":516,"createTime":18,"updateTime":18,"relativeEntities":572,"slug":18,"properties":573,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":575,"statistic":18},[],{"title":574},{"VI":521},[],{"title":577},{"VI":578},"Oguz Umut Salman",{"url":18,"publisher":580,"properties":18},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":581,"slug":10,"properties":582,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":585,"manageAffiliations":586,"indexDatabases":587,"url":23,"thumbnailPath":18,"statistic":588,"gsStatistic":18,"type":30,"analyzePriority":18},[],{"issn":583,"title":584},{"VOID":13},{"EN":15},[],[],[],{"impactFactor":19,"impactFactorByYear":589,"i10Index":19,"i10IndexLast5Year":19,"totalPublication":26,"totalPublicationByYear":590,"totalCitation":19,"totalCitationByYear":591,"totalCitationPerPublication":19,"totalCitationPerPublicationByYear":592,"hindexLast5Year":19,"hindex":19},{},{"2022":26},{},{},"2019-05-27",2019,[],{"id":597,"createTime":598,"updateTime":599,"relativeEntities":600,"slug":601,"properties":602,"entityType":52,"verifyStatus":53,"verifyTime":599,"verifyNote":55,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":611,"fullTextUrl":18,"authors":612,"publicationType":126,"publisherRelationship":667,"citationCount":18,"citationInfo":18,"publishDate":684,"publishYear":147,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":685,"openAccess":18,"references":18,"isForceReanalyzing":151},"3621645c-b379-455b-aa43-a3ec5c224462","2024-01-11T03:26:09.949+00:00","2025-02-08T21:37:35.627+00:00",[],"Phase-field-simulations-of-FCC-to-BCC-phase-transformation-in-Al-CrFeNi-medium-entropy-alloys",{"abstract":603,"title":605,"references":607,"doi":609},{"EN":604},"Microstructure simulations for quaternary alloys are still a challenge, although it is of high importance for alloy development. This work presents a Phase field (PF) approach capable of resolving phase transformation in a multicomponent system with a simple and effective way to include the thermodynamic and kinetic information for such a complex system. The microstructure evolution during diffusional transformation between FCC and BCC phase at 700 °C for AlCrFeNi alloys was simulated, accounting for composition dependence and off-diagonal terms in the diffusion tensor. The reliability of the presented PF method is validated by comparing the 1-D simulation results with simulations by Diffusion Module (DICTRA) of Thermo-Calc Software. Additionally, 2-D PF simulations of precipitate growth and Ostwald ripening are performed for different alloy systems, and the coarsening behavior is compared. Results showed that thermodynamic and kinetic information is accurately described in the applied PF method. The simulation results show that the diffusion behavior is influenced evidently by variations in the amounts of the different elements in the system. These findings demonstrate the necessity of applying accurate thermodynamic and kinetic models to fully understand the complex interdiffusion behavior in high and medium entropy alloys.",{"EN":606},"Phase field simulations of FCC to BCC phase transformation in (Al)CrFeNi medium entropy alloys",{"VOID":608},"A. Choudhury, M. Kellner, B. Nestler, A method for coupling the phase-field model based on a grand-potential formalism to thermodynamic databases. Curr. Opinion Solid State Mater. Sci. 19(5), 287–300 (2015)\nA.M. Jokisaari, P.W. Voorhees, J.E. Guyer, J. Warren, O.G. Heinonen, Benchmark problems for numerical implementations of phase field models. Comput. Mater. Sci. 126, 139–151 (2017)\nB. Cantor, I.T.H. Chang, P. Knight, A.J.B. Vincent: Microstructural development in equiatomic multicomponent alloys. Mater. Sci. Eng. A . 375-377, 213–218 (2004)\nD. Schwen, C. Jiang, L.K. Aagesen, A sublattice phase-field model for direct CALPHAD database coupling. Comput. Mater. Sci. 195, 110466 (2021)\nG. Mi, L. Xiong, C. Wang, P. Jiang, G. Zhu, Two-dimensional phase-field simulations of competitive dendritic growth during laser welding. Mater. Des. 181, 107980 (2019)\nG. Qin, R. Chen, P.K. Liaw, Y. Gao, L. Wang, Y. Su, H. Ding, J. Guo, X. Li: An as-cast high-entropy alloy with remarkable mechanical properties strengthened by nanometer precipitates. Nanoscale (2020)\nJ. Heulens, B. Blanpain, N. Moelans, A phase field model for isothermal crystallization of oxide melts. Acta Mater. 59(5), 2156–2165 (2011)\nJ. Liu, X. Guo, Q. Lin, Z. He, X. An, L. Li, P.K. Liaw, X. Liao, L. Yu, J. Lin, L. Xie, J. Ren, Y. Zhang, Excellent ductility and serration feature of metastable CoCrFeNi high-entropy alloy at extremely low temperatures. Sci. China Mater. 62(6), 853–863 (2019)\nJ.L. Li, Z. Li, Q. Wang, C. Dong, P.K. Liaw, Phase-field simulation of coherent BCC\u002FB2 microstructures in high entropy alloys. Acta Mater. 197, 10–19 (2020)\nJ-O Andersson, Thomas Helander, Lars Hoglund, Pingfang Shi, Bo Sundman: THERMO-CALC & DICTRA, Computational Tools For Material Science, Calphad 26(2), 273–312 (2002)\nJ.W. Yeh, S.K. Chen, S.J. Lin, J.Y. Gan, T.S. Chin, T.T. Shun, C.H. Tsau, S.Y. Chang, Nanostructured high-entropy alloys with multiple principal elements: Novel alloy design concepts and outcomes. Adv. Eng. Mater. 6(5), 299–303 (2004)\nK. Wu, J.E. Morral, Y. Wang: Movement of Kirkendall Markers, Second Phase Particles and the Type 0 Boundary in Two-Phase Diffusion Couple Simulations 52, 1917–1925 (2004)\nL.Q. Chen, Phase-field models for microstructure evolution. Annu. Rev. Mater. Res. 32(1), 113–140 (2002)\nM.-H. Tsai, H. Yuan, G. Cheng, W. Xu, W.W. Jian, M.-H. Chuang, C.-C. Juan, A.-C. Yeh, S.-J. Lin, Y. Zhu, Significant hardening due to the formation of a sigma phase matrix in a high entropy alloy. Intermetallics 33, 81–86 (2013)\nM.R. Tonks, D. Gaston, P.C. Millett, D. Andrs, P. Talbot, An object-oriented finite element framework for multiphysics phase field simulations. Comput. Mater. Sci. 51(1), 20–29 (2012)\nN. Moelans: http:\u002F\u002Fnele.studentenweb.org\u002Fdocs\u002Fparameters.m;http:\u002F\u002Fnele.studentenweb.org\u002Fdocs\u002FGammaDependence.txt; (2008)\nN. Moelans, B. Blanpain, P. Wollants: Quantitative analysis of grain boundary properties in a generalized phase field model for grain growth in anisotropic systems. Phys. Rev. B 78(2), 24113 (2008a)\nN. Moelans, A quantitative and thermodynamically consistent phase-field interpolation function for multi-phase systems. Acta Mater. 59(3), 1077–1086 (2011)\nN. Moelans, B. Blanpain, P. Wollants: Quantitative phase-field approach for simulating grain growth in anisotropic systems with arbitrary inclination and misorientation dependence. Phys. Rev. Lett. 101(2), 25502 (2008b)\nN. Moelans, B. Blanpain, P. Wollants, An introduction to phase-field modeling of microstructure evolution. Calphad 32, 268–294 (2008c)\nP.F. Zhou, D.H. Xiao, Z. Wu, M. Song: Microstructure and mechanical properties of AlCoCrFeNi high entropy alloys produced by spark plasma sintering. Mater. Res. Express 6(8), 0865e7 (2019)\nR. Kobayashi, Modeling and numerical simulations of dendritic crystal growth. Physica D 63(3–4), 410–423 (1993)\nR.R. Mohanty, A. Leon, Y.H. Sohn, Phase-field simulation of interdiffusion microstructure containing fcc-γ and L12-γ′ phases in Ni–Al diffusion couples. Comput. Mater. Sci. 43(2), 301–308 (2008)\nS. Chatterjee, N. Moelans, A grand-potential based phase-field approach for simulating growth of intermetallic phases in multicomponent alloy systems. Acta Mater. 206, 116630 (2021)\nS. Chen, H.S. Oh, B. Gludovatz, S.J. Kim, E.S. Park, Z. Zhang, R.O. Ritchie, Q. Yu: Real-time observations of TRIP-induced ultrahigh strain hardening in a dual-phase CrMnFeCoNi high-entropy alloy. Nat. Commun. 11(1), 826 (2020)\nS.G. Kim, A phase-field model with antitrapping current for multicomponent alloys with arbitrary thermodynamic properties. Acta Mater. 55(13), 4391–4399 (2007)\nS.G. Kim, W.T. Kim, T. Suzuki, Phase-field model for binary alloys. Phys. Rev. E 60, 7186–7196 (1999)\nS.G. Kim, W. Tae Kim, T. Suzuki, M. Ode, Phase-field modeling of eutectic solidification. J. Cryst. Growth 261(1), 135–158 (2004)\nT. Kitashima, Coupling of the phase-field and CALPHAD methods for predicting multicomponent, solid-state phase transformations. Philos. Mag. 88(11), 1615–1637 (2008)\nX. Chen, J.Q. Qi, Y.W. Sui, Y.Z. He, F.X. Wei, Q.K. Meng, Z. Sun: Effects of aluminum on microstructure and compressive properties of Al-Cr-Fe-Ni eutectic multi-component alloys. Mater. Sci. Eng. A . 681, 25–31 (2017)\nX. Jin, J. Bi, L. Zhang, Y. Zhou, X. Du, Y. Liang, B. Li, A new CrFeNi2Al eutectic high entropy alloy system with excellent mechanical properties. J. Alloy Compd. 770, 655–661 (2019)\nX.H. Du, W.P. Li, H.T. Chang, T. Yang, G.S. Duan, B.L. Wu, J.C. Huang, F.R. Chen, C.T. Liu, W.S. Chuang, Y. Lu, M.L. Sui, E.W. Huang: Dual heterogeneous structures lead to ultrahigh strength and uniform ductility in a Co-Cr-Ni medium-entropy alloy. Nat. Commun. 11(1), 2390 (2020)\nY.A. Coutinho, N. Vervliet, L. de Lathauwer, N. Moelans: Combining thermodynamics with tensor completion techniques to enable multicomponent microstructure prediction. NPJ Comput Mater 6(1) (2020)\nY.P. Wang, B.S. Li, M.X. Ren, C. Yang, H.Z. Fu: Microstructure and compressive properties of AlCrFeCoNi high entropy alloy. Mater. Sci. Eng. A . 491(1–2), 154–158 (2008)\nY. Dong, X. Gao, Y. Lu, T. Wang, T. Li, A multi-component AlCrFe2Ni2 alloy with excellent mechanical properties. Mater. Lett. 169, 62–64 (2016)\nY. Yuan, Y. Wu, X. Tong, H. Zhang, H. Wang, X.J. Liu, L. Ma, H.L. Suo, Z.P. Lu, Rare-earth high-entropy alloys with giant magnetocaloric effect. Acta Mater. 125, 481–489 (2017)\nZ. Jiang, W. Chen, Z. Xia, W. Xiong, Z. Fu, Influence of synthesis method on microstructure and mechanical behavior of co-free AlCrFeNi medium-entropy alloy. Intermetallics 108, 45–54 (2019)",{"VOID":610},"10.1186\u002Fs41313-021-00034-4","https:\u002F\u002Fmaterialstheory.springeropen.com\u002Farticles\u002F10.1186\u002Fs41313-021-00034-4",[613,628,641,654],{"id":614,"sortIndex":19,"researcher":18,"roles":615,"affiliations":616,"properties":625,"displayName":627,"givenName":18,"familyName":18},"e9975636-38a3-485f-a385-d761cc70899c",[61],[617],{"id":618,"sortIndex":19,"affiliation":619,"properties":18},"b299883a-e07b-4539-803b-1f2288ddd54c",{"id":618,"createTime":18,"updateTime":18,"relativeEntities":620,"slug":18,"properties":621,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":624,"statistic":18},[],{"title":622},{"VI":623},"Department of Materials Engineering, KU Leuven, Leuven, Belgium",[],{"title":626},{"VI":627},"X. J. Zuo",{"id":629,"sortIndex":26,"researcher":18,"roles":630,"affiliations":631,"properties":638,"displayName":640,"givenName":18,"familyName":18},"4e3cf566-1d55-4a77-aecd-8e9c12ce5a5e",[61],[632],{"id":618,"sortIndex":19,"affiliation":633,"properties":18},{"id":618,"createTime":18,"updateTime":18,"relativeEntities":634,"slug":18,"properties":635,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":637,"statistic":18},[],{"title":636},{"VI":623},[],{"title":639},{"VI":640},"Y. Coutinho",{"id":642,"sortIndex":100,"researcher":18,"roles":643,"affiliations":644,"properties":651,"displayName":653,"givenName":18,"familyName":18},"114c8269-b77a-4475-8931-748b1bd25484",[61],[645],{"id":618,"sortIndex":19,"affiliation":646,"properties":18},{"id":618,"createTime":18,"updateTime":18,"relativeEntities":647,"slug":18,"properties":648,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":650,"statistic":18},[],{"title":649},{"VI":623},[],{"title":652},{"VI":653},"S. Chatterjee",{"id":655,"sortIndex":114,"researcher":18,"roles":656,"affiliations":657,"properties":664,"displayName":666,"givenName":18,"familyName":18},"3671e85f-45a1-4dd4-9f5a-12156f875699",[61],[658],{"id":618,"sortIndex":19,"affiliation":659,"properties":18},{"id":618,"createTime":18,"updateTime":18,"relativeEntities":660,"slug":18,"properties":661,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":663,"statistic":18},[],{"title":662},{"VI":623},[],{"title":665},{"VI":666},"N. Moelans",{"url":611,"publisher":668,"properties":681},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":669,"slug":10,"properties":670,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":673,"manageAffiliations":674,"indexDatabases":675,"url":23,"thumbnailPath":18,"statistic":676,"gsStatistic":18,"type":30,"analyzePriority":18},[],{"issn":671,"title":672},{"VOID":13},{"EN":15},[],[],[],{"impactFactor":19,"impactFactorByYear":677,"i10Index":19,"i10IndexLast5Year":19,"totalPublication":26,"totalPublicationByYear":678,"totalCitation":19,"totalCitationByYear":679,"totalCitationPerPublication":19,"totalCitationPerPublicationByYear":680,"hindexLast5Year":19,"hindex":19},{},{"2022":26},{},{},{"pages":682,"volume":683},{"VOID":417},{"VOID":145},"2022-03-07",[],{"id":687,"createTime":688,"updateTime":689,"relativeEntities":690,"slug":691,"properties":692,"entityType":52,"verifyStatus":53,"verifyTime":701,"verifyNote":55,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":702,"fullTextUrl":18,"authors":703,"publicationType":126,"publisherRelationship":762,"citationCount":18,"citationInfo":18,"publishDate":781,"publishYear":782,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":783,"openAccess":18,"references":18,"isForceReanalyzing":151},"9c37939c-7978-44b4-a91e-1833255e1e18","2024-01-24T11:46:36.100+00:00","2025-02-06T19:39:35.602+00:00",[],"Mixed-mode-growth-of-a-multicomponent-precipitate-in-the-quasi-steady-state-regime",{"abstract":693,"title":695,"references":697,"doi":699},{"EN":694},"An exact analytical solution of the Fick’s second law was developed and applied to the mixed-mode growth of a multicomponent ellipsoidal precipitate growing with constant eccentricities in the quasi-stationary regime. The solution is exact if the nominal composition, equilibrium concentrations and material properties are assumed constant, and can be applied to compounds having no limitations in the number of components. The solution was compared to the solution calculated by a diffusion-controlled application software and it was found that the solute concentrations at the interface can be determined knowing only the nominal composition, the full equilibrium concentrations and the coefficients of diffusion. The thermodynamic calculations owing to find alternative tie-lines are proven to be useless in the mixed-mode model. From this, it appears that the search of alternative tie-lines is computationally counterproductive, even when the interface has a very high mobility. A more efficient computational scheme is possible by considering that a moving interface is not at equilibrium.",{"EN":696},"Mixed-mode growth of a multicomponent precipitate in the quasi-steady state regime",{"VOID":698},"HI Aaronson, M Enomoto, JK Lee, Mechanisms of Diffusional Phase Transformations in Metals and Alloys (Taylor & Francis, Boca Raton, 2010)\nJ Ågren, Numerical treatment of diffusional reactions in multicomponent alloys. J. Phys. Chem. Solids 43(4), 385–391 (1982)\nJ-O Andersson, T Helander, L Hoglund, P Shi, B Sundman, Thermo-Calc & DICTRA, computational tools for materials science. Calphad 26(2), 273–312 (2002)\nQ Chen, J Jeppsson, J Ågren, Analytical treatment of diffusion during precipitate growth in multicomponent systems. Acta Materi. 56, 1890–1896 (2008)\nJW Christian, The Theory of Transformations in Metals and Alloys; an Advanced Textbook in Physical Metallurgy (Pergamon Press, Oxford, 1965)\nG Ghosh, G Olson, Precipitation of paraequilibrium cementite: Experiments, and thermodynamic and kinetic modeling. Acta Mater. 50(8), 2099–2119 (2002)\nFS Ham, Shape-preserving solutions of the time-dependent diffusion equation. Q. Appl. Math. 17(2), 137–145 (1959)\nM Hillert, Phase Equilibria, Phase Diagrams and Phase Transformations: Their Thermodynamic Basis, 2nd edn. (Cambridge University Press, Cambridge, 2008)\nG Horvay, JW Cahn, Dendritic and spheroidal growth. Acta Metall. 9(7), 695–705 (1961)\nCA Johnson, Generalization of the Gibbs-Thomson equation. Surf. Sci. 3(5), 429–444 (1965)\nJS Kirkaldy, Diffusion in multicomponent metallic systems .2. Solutions for 2-phase systems with applications to transformations in steel. Can. J. Phys. 36(7), 907–916 (1958)\nE Kozeschnik, Modeling Solid-State Precipitation (Momentum Press, New York, 2013a)\nE Kozeschnik, Modeling Solid-State Precipitation, vol 131 (Momentum Press, New York, 2013b)\nE Kozeschnik, J Svoboda, P Fratzl, FD Fischer, Modelling of kinetics in multi-component multi-phase systems with spherical precipitates: II: Numerical solution and application. Mater. Sci. Eng. A 385, 157–165 (2004)\nD Larouche, Mixed mode growth of an ellipsoidal precipitate: Analytical solution for shape preserving growth in the quasi-stationary regime. Acta Mater. 123, 188–196 (2017)\nE. Povoden-Karadeniz. Physical Properties Data from MatCalc Database 'mc_al.pdb' Version 1.019, (2012).\nE. Povoden-Karadeniz. Thermodynamic Data from MatCalc Database 'mc_al.tdb',Version 2.030, (2015a).\nE. Povoden-Karadeniz. 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A faithful physicochemical understanding of these processes is crucial for the design and synthesis of chemicals and materials of value for our society and economy. Although some problems in this field can be adequately addressed by classical mechanics, many demand an explicit quantum mechanical description. Such quantum problems require a representation of wave functions that grows exponentially with system size and therefore should naturally benefit from quantum computation on a number of logical qubits that scales only linearly with system size. In this perspective, we elaborate on the potential benefits of quantum computing in the molecular sciences, i.e., in molecular physics, chemistry, biochemistry, and materials science.\u003C\u002Fjats:p>",{"EN":798},"Prospects of quantum computing for molecular sciences",{"VOID":800},"10.1186\u002Fs41313-021-00039-z",[802],"EN","https:\u002F\u002Fmaterialstheory.springeropen.com\u002Farticles\u002F10.1186\u002Fs41313-021-00039-z",[805,824,839,856,871,890],{"id":806,"sortIndex":19,"researcher":18,"roles":807,"affiliations":808,"properties":817,"displayName":821,"givenName":18,"familyName":18},"69830daa-e333-4992-bbc6-2ec72e064efe",[],[809],{"id":810,"sortIndex":19,"affiliation":811,"properties":18},"42be354d-178a-4713-97c7-27deceaadbaf",{"id":810,"createTime":18,"updateTime":18,"relativeEntities":812,"slug":18,"properties":813,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":816,"statistic":18},[],{"title":814},{"EN":815},"Microsoft Quantum, Redmond, Washington, 98052, USA",[],{"orcid":818,"title":820,"openalex":822},{"VOID":819},"https:\u002F\u002Forcid.org\u002F0000-0002-3892-1636",{"EN":821},"Hongbin Liu",{"VOID":823},"A5056183335",{"id":825,"sortIndex":26,"researcher":18,"roles":826,"affiliations":827,"properties":834,"displayName":836,"givenName":18,"familyName":18},"89bf9e64-dbdb-46d0-a7fc-594ff86ec446",[],[828],{"id":810,"sortIndex":19,"affiliation":829,"properties":18},{"id":810,"createTime":18,"updateTime":18,"relativeEntities":830,"slug":18,"properties":831,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":833,"statistic":18},[],{"title":832},{"EN":815},[],{"title":835,"openalex":837},{"EN":836},"Guang Hao Low",{"VOID":838},"A5019343939",{"id":840,"sortIndex":100,"researcher":18,"roles":841,"affiliations":842,"properties":851,"displayName":853,"givenName":18,"familyName":18},"727e5817-db4c-4b4d-9ea6-315006f67581",[],[843],{"id":844,"sortIndex":19,"affiliation":845,"properties":18},"f94e47a9-3ca6-4331-a177-af253ebb29ab",{"id":844,"createTime":18,"updateTime":18,"relativeEntities":846,"slug":18,"properties":847,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":850,"statistic":18},[],{"title":848},{"EN":849},"Microsoft Quantum, Zürich, 8038, Switzerland",[],{"title":852,"openalex":854},{"EN":853},"Damian S. 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Science. 370(6523), 1460–1463 (2020).",{"doi":1395},"10.1126\u002Fscience.abe8770",{"id":1397,"createTime":1398,"updateTime":1399,"relativeEntities":1400,"slug":1401,"properties":1402,"entityType":52,"verifyStatus":53,"verifyTime":1399,"verifyNote":55,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1411,"fullTextUrl":18,"authors":1412,"publicationType":126,"publisherRelationship":1452,"citationCount":18,"citationInfo":18,"publishDate":1470,"publishYear":782,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":1471,"openAccess":18,"references":18,"isForceReanalyzing":151},"ae589793-b340-4386-a4fd-0d18a8e1b65e","2024-01-14T10:54:01.346+00:00","2025-01-31T00:08:29.295+00:00",[],"An-analysis-of-two-classes-of-phase-field-models-for-void-growth-and-coarsening-in-irradiated-crystalline-solids",{"abstract":1403,"title":1405,"references":1407,"doi":1409},{"EN":1404},"A formal asymptotic analysis of two classes of phase field models for void growth and coarsening in irradiated solids has been performed to assess their sharp-interface kinetics. It was found that the sharp interface limit of type B models, which include only point defect concentrations as order parameters governed by Cahn-Hilliard equations, captures diffusion-controlled kinetics. It was also found that a type B model reduces to a generalized one-sided classical Stefan problem in the case of a high driving thermodynamic force associated with the void growth stage, while it reduces to a generalized one-sided Mullins-Sekerka problem when the driving force is low in the case of void coarsening. The latter case corresponds to the famous rate theory description of void growth. Type C models, which include point defect concentrations and a non-conserved order parameter to distinguish between the void and solid phases and employ coupled Cahn-Hilliard and Allen-Cahn equations, are shown to represent mixed diffusion and interfacial kinetics. In particular, the Allen-Cahn equation of model C reduces to an interfacial constitutive law representing the attachment and emission kinetics of point defects at the void surface. In the limit of a high driving force associated with the void growth stage, a type C model reduces to a generalized one-sided Stefan problem with kinetic drag. In the limit of low driving forces characterizing the void coarsening stage, however, the model reduces to a generalized one-sided Mullins-Sekerka problem with kinetic drag. The analysis presented here paves the way for constructing quantitative phase field models for the irradiation-driven nucleation and growth of voids in crystalline solids by matching these models to a recently developed sharp interface theory.",{"EN":1406},"An analysis of two classes of phase field models for void growth and coarsening in irradiated crystalline solids",{"VOID":1408},"K Ahmed, T Allen, A El-Azab, J. Mater. 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Mater. 439, 25 (2013)\nH Yu, W Lu, Acta Mater. 53, 1799 (2005)",{"VOID":1410},"10.1186\u002Fs41313-017-0008-y","https:\u002F\u002Fmaterialstheory.springeropen.com\u002Farticles\u002F10.1186\u002Fs41313-017-0008-y",[1413,1437],{"id":1414,"sortIndex":19,"researcher":18,"roles":1415,"affiliations":1416,"properties":1434,"displayName":1436,"givenName":18,"familyName":18},"86723f5a-edb8-456b-a4ce-fe583314558e",[61],[1417,1425],{"id":1418,"sortIndex":19,"affiliation":1419,"properties":18},"32139ef0-2742-4a94-a8da-6985b2fddd39",{"id":1418,"createTime":18,"updateTime":18,"relativeEntities":1420,"slug":18,"properties":1421,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1424,"statistic":18},[],{"title":1422},{"VI":1423},"School of Nuclear Engineering, Purdue University, West Lafayette, USA",[],{"id":1426,"sortIndex":26,"affiliation":1427,"properties":1433},"8028aa71-d258-4db1-9e9d-7719ba20d7c1",{"id":1426,"createTime":18,"updateTime":18,"relativeEntities":1428,"slug":18,"properties":1429,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1432,"statistic":18},[],{"title":1430},{"VI":1431},"Present address: Department of Nuclear Engineering, Texas A&M University, College Station, USA",[],{},{"title":1435},{"VI":1436},"K. Ahmed",{"id":1438,"sortIndex":26,"researcher":18,"roles":1439,"affiliations":1440,"properties":1449,"displayName":1451,"givenName":18,"familyName":18},"d78c2463-f964-447f-8ddd-4ec86dde53e7",[61],[1441],{"id":1442,"sortIndex":19,"affiliation":1443,"properties":18},"b1199f4c-4622-439e-b280-6af0d3612e26",{"id":1442,"createTime":18,"updateTime":18,"relativeEntities":1444,"slug":18,"properties":1445,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1448,"statistic":18},[],{"title":1446},{"VI":1447},"School of Materials Engineering, Purdue University, West Lafayette, USA",[],{"title":1450},{"VI":1451},"A. El-Azab",{"url":1411,"publisher":1453,"properties":1466},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1454,"slug":10,"properties":1455,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":1458,"manageAffiliations":1459,"indexDatabases":1460,"url":23,"thumbnailPath":18,"statistic":1461,"gsStatistic":18,"type":30,"analyzePriority":18},[],{"issn":1456,"title":1457},{"VOID":13},{"EN":15},[],[],[],{"impactFactor":19,"impactFactorByYear":1462,"i10Index":19,"i10IndexLast5Year":19,"totalPublication":26,"totalPublicationByYear":1463,"totalCitation":19,"totalCitationByYear":1464,"totalCitationPerPublication":19,"totalCitationPerPublicationByYear":1465,"hindexLast5Year":19,"hindex":19},{},{"2022":26},{},{},{"pages":1467,"volume":1469},{"VOID":1468},"1-36",{"VOID":780},"2018-01-31",[]]