Data Density as Discovery Bias: Uneven Sampling in Computational Materials Exploration

iamrp.net-Aab6-1791483545-n845559228 1791483545 Institute for Advanced Materials Research Press info@iamrp.net Institute for Advanced Materials Research Press Journal of Computational and Data-Driven Materials Engineering J. Comput. Data-Driven Mater. Eng. 3149-9368 2022 1 2 Data Density as Discovery Bias: Uneven Sampling in Computational Materials Exploration Fatima Zahra Amrani Youssef Benali 2022 10.68159/n845559228 https://iamrp.net/pub/journal/2/article/n845559228 Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C. Machine learning in materials informatics: Recent applications and prospects. npj Comput Mater. 2017;3(1):54. Schmidt J, Marques MRG, Botti S, Marques MAL. Recent advances and applications of machine learning in solid-state materials science. npj Comput Mater. 2019;5(1):83. 10.1038/s41524-019-0221-0 Pilania G, Gubernatis JE, Lookman T. Multi-fidelity machine learning models for accurate bandgap predictions of solids. Comput Mater Sci. 2017;129:156-63. 10.1016/j.commatsci.2016.12.004 Jensen S, Jacobsen TCS, Reuter K, Thygesen KS. Data-driven discovery of 2D materials by deep generative models. npj Computat Mater. 2022;8(1):232. 10.1038/s41524-022-00923-3 Chen L, Tran H, Batra R, Kim C, Ramprasad R. Machine learning models for the prediction of energy, forces, and stresses for molecules and materials. npj Comput Mater. 2021;7(1):19. Zheng P, Liu H, Wang J, Yu B, Gu X, Lu S. Enhancing geometric representations for molecules with equivariant transformer networks. npj Comput Mater. 2021;7(1):200. Dan Y, Zhao Y, Li X, Li S, Hu M, Hu J. Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse materials design. npj Comput Mater. 2020;6(1):84 Zheng Z, Xu Z, Hu Y, Yaghi OM. Machine-Learning-guided morphology engineering of nanoscale metal-organic frameworks. Matter. 2020;3(4):1104-14. Lookman T, Balachandran PV, Xue D, Yuan R. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. npj Comput Mater. 2019;5(1):21. 10.1038/s41524-019-0153-8 Jennings PC, Lysgaard S, Hummelshøj JS, Vegge T, Bligaard T. Genetic algorithms for computational materials discovery accelerated by machine learning. npj Comput Mater. 2019;5(1):46. 10.1038/s41524-019-0181-4 Rosen AS, Iyer SM, Ray D, Yao Z, Aspuru-Guzik A, Gagliardi L, et al. Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery. Matter. 2021;4(5):1578-97. 10.1016/j.matt.2021.02.015 Huang W, Martin P, Zhuang HL. Machine-learning phase prediction of high-entropy alloys. Acta Mater. 2019;169:225-36. 10.1016/j.actamat.2019.03.012 Zou C, Li J, He Q, Liang D, Luo Y, Tong H, et al. Integrating data mining and machine learning to discover high-strength ductile titanium alloys. Acta Mater. 2021;202:211-21. 10.1016/j.actamat.2020.10.056 Park CW, Wolverton C. Self-supervised machine learning for alloy composition prediction using x-ray absorption spectroscopy. npj Comput Mater. 2022;8(1):1. Li X, Kailkhura B, Gallagher B, Kim S, Hiszpanski A, Yao Y. Explainable machine learning in materials science. npj Comput Mater. 2022;8(1):204. 10.1038/s41524-022-00884-7 Saidi P, Zadkhast P, Sasani F, Shad E, Srivastava A. Machine learning-enabled discrete element method: A parallel computing perspective. Comput Mater Sci. 2021;197:110626. Kim G, Diao H, Lee C, Samaei AT, Phan T, de Jong M, et al. First-principles and machine learning predictions of elasticity in severely lattice-distorted high-entropy alloys with experimental validation. Acta Mater. 2019;181:124-38. 10.1016/j.actamat.2019.09.026 Vazquez G, Singh P, Sauceda D, Batzner S, Kozinsky B. Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys. Acta Mater. 2022;232:117927. 10.1016/j.actamat.2022.117927 Möller JJ, Körner W, Krugel G, Urban DF, Elsässer C. Compositional optimization of hard-magnetic phases with machine-learning models. Acta Mater. 2018;153:53-61. 10.1016/j.actamat.2018.03.051 Dunn A, Wang Q, Ganpule S, Wang D, Jain A. Benchmarking materials property prediction methods: The matbench test set and automatminer reference algorithm. npj Comput Mater. 2020;6(1):138. 10.1038/s41524-020-00406-3 Saeki A, Ueda M, Matsunaga Y, Furusawa M, Hui JK, Kim MW, et al. Machine learning identification of experimental conditions for the synthesis of single-phase white phosphors. Matter. 2021;4(12):4040-57. 10.1016/j.matt.2021.10.004 Fung V, Hu G, Ganesh P, Sumpter BG. Machine learned features from density of states for accurate adsorption energy prediction. Nat Commun. 2021;12(1):88.