Chen Wei-Chih, Vohra Yogesh K, Chen Cheng-Chien
Department of Physics, University of Alabama at Birmingham, Birmingham, Alabama 35294, United States.
ACS Omega. 2022 Jun 9;7(24):21035-21042. doi: 10.1021/acsomega.2c01818. eCollection 2022 Jun 21.
We searched for new superhard B-N-O compounds with an iterative machine learning (ML) procedure, where ML models are trained using sample crystal structures from an evolutionary algorithm. We first used cohesive energy to evaluate the thermodynamic stability of varying B N O compositions and then gradually focused on compositional regions with high cohesive energy and high hardness. The results converged quickly after a few iterations. Our resulting ML models show that B N O compounds with ≥ 3 (like BNO, BNO, etc.) are potentially superhard and thermodynamically favorable. Our meta-GGA density functional theory calculations indicate that these materials are also wide bandgap (≥4.4 eV) insulators, with the valence band maximum related to the p-orbitals of nitrogen atoms near vacant sites. This study demonstrates that an iterative method combining ML and simulations provides a powerful tool for discovering novel materials.
我们通过迭代机器学习（ML）程序寻找新的超硬B-N-O化合物，其中ML模型使用来自进化算法的样本晶体结构进行训练。我们首先使用内聚能来评估不同B-N-O组成的热力学稳定性，然后逐渐关注具有高内聚能和高硬度的组成区域。经过几次迭代后结果迅速收敛。我们得到的ML模型表明，B≥3的B-N-O化合物（如BNO、BNO等）具有潜在的超硬性且在热力学上是有利的。我们的元广义梯度近似密度泛函理论计算表明，这些材料也是宽带隙（≥4.4 eV）绝缘体，价带最大值与空位附近氮原子的p轨道有关。这项研究表明，结合ML和模拟的迭代方法为发现新型材料提供了一个强大的工具。
