Mechanical properties of single and polycrystalline solids from machine learning

Calculations of elastic and mechanical characteristics of non-crystalline solids are challenging due to high computation cost of \(ab\) \(initio\) methods and low accuracy of empirical potentials. We propose a computational technique towards efficient calculations of mechanical properties of polycry...

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Bibliographic Details
Published in:arXiv.org 2023-09
Main Authors: Jalolov, Faridun N, Podryabinkin, Evgeny V, Oganov, Artem R, Shapeev, Alexander V, Kvashnin, Alexander G
Format: Article
Language:English
Subjects:
Accuracy
Computing costs
Grain size
Machine learning
Mathematical analysis
Mechanical properties
Modulus of elasticity
Polycrystalline diamond
Polycrystals
Single crystals
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Summary:Calculations of elastic and mechanical characteristics of non-crystalline solids are challenging due to high computation cost of \(ab\) \(initio\) methods and low accuracy of empirical potentials. We propose a computational technique towards efficient calculations of mechanical properties of polycrystals, composites, and multi-phase systems from atomistic simulation with high accuracy and reasonable computational cost. It is based on using actively learned machine learning interatomic potentials (MLIPs) trained on a local fragments of the polycrystalline system for which forces, stresses and energies are computed by using \(ab\) \(initio\) calculations. Developed approach is used for calculation the dependence of elastic moduli of polycrystalline diamond on the grain size. This technique allows one to perform large-scale calculations of mechanical properties of complex solids of various compositions and structures with high accuracy making the transition from ideal (single crystal) systems to more realistic ones.
ISSN:2331-8422