材料科学
人工神经网络
理论(学习稳定性)
带隙
回归
统计物理学
立方体(代数)
生物系统
计算机科学
机器学习
数学
统计
光电子学
生物
组合数学
物理
作者
Mathew J. Cherukara,Arun Mannodi‐Kanakkithodi
标识
DOI:10.1088/1361-651x/ac52de
摘要
Abstract The ability to accurately and quickly predict the stability of materials and their structural and electronic properties remains a grand challenge in materials science. Density functional theory is widely used as a means of predicting these material properties, but is known to be computationally expensive and scales as the cube of the number of electrons in the material’s unit cell. In this article, for a previously published dataset of inorganic perovskites, we show that a single neural network model using only the elemental properties of the compounds’ constituents can predict lattice constants to within 0.1 Å, heat of formation to within 0.2 eV, and band gaps to within 0.7 eV RMSE. We also compare the performance of the trained network to two widely used regression techniques, namely random forest and Kernel ridge regression, and find that the neural network’s predictions are more accurate for each of the properties. The simultaneous accurate prediction of multiple key properties of technologically relevant materials is promising for rational design and optimization in known and novel chemical spaces.
科研通智能强力驱动
Strongly Powered by AbleSci AI