MechElastic: A Python Library for Analysis of Mechanical and Elastic Properties of Bulk and 2D Materials

Singh, Sobhit; Lang, Logan; Dovale-Farelo, Viviana; Herath, Uthpala; Tavadze, Pedram; Coudert, François-Xavier; Romero, Aldo H.

doi:10.48550/arxiv.2012.04758

Cited by 1 publication

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References 88 publications

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“…For fully relaxed crystal structures, we further utilize the strain-stress method [52] implemented in VASP to compute the elastic constants C ij , which in turn help to evaluate the elastic stability and mechanical properties. In particular, the eigenvalues of the elastic tensor (for elastic stability), as well as the Voigt-Reuss-Hill (VRH) averaged [53][54][55] bulk modulus K and shear modulus G, are computed via the MechElastic python library [56,57]. The Vickers hardness H is also evaluated by using Chen's empirical hardness model [58]:…”

Section: Methodsmentioning

confidence: 99%

Discovering Superhard B-N-O Compounds by Iterative Machine Learning and Evolutionary Structure Predictions

Chen,

Vohra,

Chen

2021

Preprint

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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 BxNyOz 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 Bx+2NxO3 compounds with x ≥ 3 (like B5N3O3, B6N4O3, 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 ab initio simulations provides a powerful tool for discovering novel materials.

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Section: Methodsmentioning

confidence: 99%

Discovering Superhard B-N-O Compounds by Iterative Machine Learning and Evolutionary Structure Predictions

Chen,

Vohra,

Chen

2021

Preprint

View full text Add to dashboard Cite

show abstract

MechElastic: A Python Library for Analysis of Mechanical and Elastic Properties of Bulk and 2D Materials

Abstract: as Murnaghan, Birch, Birch-Murnaghan, and Vinet, by reading the inputted energy/pressure versus volume data obtained via numerical calculations or experiments. This package is particularly useful for the high-throughput analysis of elastic and mechanical properties of materials.

Cited by 1 publication

References 88 publications

Discovering Superhard B-N-O Compounds by Iterative Machine Learning and Evolutionary Structure Predictions

Discovering Superhard B-N-O Compounds by Iterative Machine Learning and Evolutionary Structure Predictions

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