Benchmark AFLOW Data Sets for Machine Learning

Donoho D (2017) 50 years of data science. J Comput Gr Stat 26(4):745–766

Seshadri R, Sparks TD (2016) Perspective: interactive material property databases through aggregation of literature data. APL Mater 4(5):053206

Curtarolo S, Setyawan W, Hart GLW, Jahnatek M, Chepulskii RV, Taylor RH, Wang S, Xue J, Yang K, Levy O et al (2012) AFLOW: an automatic framework for high-throughput materials discovery. Comput Mater Sci 58:218–226

Jain A, Ong SP, Hautier G, Chen W, Richards WD, Dacek S, Cholia S, Gunter D, Skinner D, Ceder G et al (2013) Commentary: the materials project—a materials genome approach to accelerating materials innovation. Apl Mater 1(1):011002

Hellenbrandt M (2004) The inorganic crystal structure database (ICSD)—present and future. Crystallogr Rev 10(1):17–22

Saal JE, Kirklin S, Aykol M, Meredig B, Wolverton C (2013) Materials design and discovery with high-throughput density functional theory: the open quantum materials database (OQMD). JOM 65(11):1501–1509

Hill J, Mulholland G, Persson K, Seshadri R, Wolverton C, Meredig B (2016) Materials science with large-scale data and informatics: unlocking new opportunities. MRS Bull 41(5):399–409

Ward L, Dunn A, Faghaninia A, Zimmermann NER, Bajaj S, Wang Q, Montoya J, Chen J, Bystrom K, Dylla M et al (2018) Matminer: an open source toolkit for materials data mining. Comput Mater Sci 152:60–69

Ong SP, Richards WD, Jain A, Hautier G, Kocher M, Cholia S, Gunter D, Chevrier VL, Persson KA, Ceder G (2013) Python materials genomics (pymatgen): a robust, open-source python library for materials analysis. Comput Mater Sci 68:314–319

Giannozzi P, Baroni S, Bonini N, Calandra M, Car R, Cavazzoni C, Ceresoli D, Chiarotti GL, Cococcioni M, Dabo I et al (2009) Quantum ESPRESSO: a modular and open-source software project for quantum simulations of materials. J Phys Condens Matter 21(39):395502

Citrination. www.citrination.com

Schmidt J, Marques MRG, Botti S, Marques MAL (2019) Recent advances and applications of machine learning in solid-state materials science. NPJ Comput Mater 5(1):1–36

Olson RS, La Cava W, Orzechowski P, Urbanowicz RJ, Moore JH (2017) PMLB: a large benchmark suite for machine learning evaluation and comparison. BioData Min 10(1):36

Deng L (2012) The MNIST database of handwritten digit images for machine learning research [best of the web]. IEEE Signal Process Mag 29(6):141–142

Krizhevsky A, Nair V, Hinton G, CIFAR-10 and CIFAR-100 datasets. www.cs.toronto.edu/kriz/cifar.html

Kauwe SK, Welker T, Sparks TD (2018) Extracting knowledge from dft: experimental band gap predictions through ensemble learning. https://doi.org/10.26434/chemrxiv.7236029

Zhuo Y, Tehrani AM, Brgoch J (2018) Predicting the band gaps of inorganic solids by machine learning. J Phys Chem Lett 9(7):1668–1673

Zhang Y, Kitchaev DA, Yang J, Chen T, Dacek ST, Sarmiento-Pérez RA, Marques MAL, Peng H, Ceder G, Perdew JP et al (2018) Efficient first-principles prediction of solid stability: towards chemical accuracy. NPJ Comput Mater 4(1):1–6

Murdock R, Kauwe S, Wang A, Sparks T (2020) Is domain knowledge necessary for machine learning materials properties? https://doi.org/10.26434/chemrxiv.11879193.v1

Hall SR, Allen FH, Brown ID (1991) The crystallographic information file (CIF): a new standard archive file for crystallography. Acta Crystallogr A 47(6):655–685

Xie T, Grossman JC (2018) Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys Rev Lett 120(14):145301

Schütt KT, Sauceda HE, Kindermans P-J, Tkatchenko A, Müller K-R (2018) SchNet—a deep learning architecture for molecules and materials. J Chem Phys 148(24):241722

Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction. Springer, Berlin