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- W4387209711 abstract "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." @default.
- W4387209711 created "2023-10-01" @default.
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- W4387209711 date "2023-09-26" @default.
- W4387209711 modified "2023-10-01" @default.
- W4387209711 title "Mechanical properties of single and polycrystalline solids from machine learning" @default.
- W4387209711 doi "https://doi.org/10.48550/arxiv.2309.15868" @default.
- W4387209711 hasPublicationYear "2023" @default.
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