Discovering Superhard B-N-O Compounds by Iterative Machine Learning and Evolutionary Structure Predictions
arXiv:2111.12923 · doi:10.1021/acsomega.2c01818
Abstract
We search for new superhard B-N-O compounds with an iterative machine learning (ML) procedure, where ML models are trained using sample crystal structures from evolutionary algorithm. We first use cohesive energy to evaluate the thermodynamic stability of varying BNO compositions, and then gradually focus on compositional regions with high cohesive energy and high hardness. The results converge quickly after a few iterations. Our resulting ML models show that BNO compounds with (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 ( eV) insulators, with the valence band maximum related to the -orbitals of nitrogen atoms near vacant sites. This study demonstrates that an iterative method combining ML and ab initio simulations provides a powerful tool for discovering novel materials.
8 pages, 5 figures
References in corpus (6)
- Necessary and Sufficient Elastic Stability Conditions in Various Crystal Systems
- Intrinsic Correlation between Hardness and Elasticity in Polycrystalline Materials and Bulk Metallic Glasses
- The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design
- MechElastic: A Python Library for Analysis of Mechanical and Elastic Properties of Bulk and 2D Materials
- Superhard Phases of Simple Substances and Binary Compounds of the B-C-N-O System: from Diamond to the Latest Results (a Review)
- Machine Learning and Evolutionary Prediction of Superhard B-C-N Compounds