Deep learning potential for superionic phase of Ag2S

Balyakin, I. A.; Садовников, С. И.

doi:10.1016/j.commatsci.2021.110963

Cited by 17 publications

(9 citation statements)

References 50 publications

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“…For this purpose, larger spatial/ time scale than FPMD and higher accuracy than classical MD is required. With a recent progress of machineleaning interatomic potentials (MLIPs), such large-scale molecular simulations of metal chalcogenides with high accuracy are becoming feasible [24][25][26][27][28] . We plan to apply advanced computational techniques such as MLIPs for further investigations of Ag 2 S systems.…”

Section: Resultsmentioning

confidence: 99%

Defect-free and crystallinity-preserving ductile deformation in semiconducting Ag2S

Misawa

Hokyo

Fukushima

et al. 2022

Sci Rep

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Typical ductile materials are metals, which deform by the motion of defects like dislocations in association with non-directional metallic bonds. Unfortunately, this textbook mechanism does not operate in most inorganic semiconductors at ambient temperature, thus severely limiting the development of much-needed flexible electronic devices. We found a shear-deformation mechanism in a recently discovered ductile semiconductor, monoclinic-silver sulfide (Ag2S), which is defect-free, omni-directional, and preserving perfect crystallinity. Our first-principles molecular dynamics simulations elucidate the ductile deformation mechanism in monoclinic-Ag2S under six types of shear systems. Planer mass movement of sulfur atoms plays an important role for the remarkable structural recovery of sulfur-sublattice. This in turn arises from a distinctively high symmetry of the anion-sublattice in Ag2S, which is not seen in other brittle silver chalcogenides. Such mechanistic and lattice-symmetric understanding provides a guideline for designing even higher-performance ductile inorganic semiconductors.

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Section: Resultsmentioning

confidence: 99%

Defect-free and crystallinity-preserving ductile deformation in semiconducting Ag2S

Misawa

Hokyo

Fukushima

et al. 2022

Sci Rep

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“…These DP-based simulations provided a starting point for large size scale and long time scale MD investigations of solid-state electrolyte materials. Additional DPs were developed for a wide-range of other multi-element bulk systems, including metal oxide [163][164][165], metal sulfide [166,167], thermoelectric SnSe materials [168], metal borides [169,171], and metal carbide [170] systems.…”

Section: Multi-element Bulk Systemsmentioning

confidence: 99%

Deep potentials for materials science

Wen

Zhang²,

Wang

et al. 2022

Mater. Futures

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To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials, a new class of descriptions of atomic interactions has emerged and been widely applied; i.e., machine learning potentials (MLPs). One recently developed type of MLP is the Deep Potential (DP) method. In this review, we provide an introduction to DP methods in computational materials science. The theory underlying the DP method is presented along with a step-by-step introduction to their development and use. We also review materials applications of DPs in a wide range of materials systems. The DP Library provides a platform for the development of DPs and a database of extant DPs. We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.

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“…30 Because of its excellent performance and flexibility, deep learning interatomic potentials have enabled growing popularity in both chemistry and materials science, such as fuel oxidation, 31,32 phase change, 29,33 chemical catalysis 34 and material design. 35,36 In this work, a NN-based potential (NNP) model is developed to examine the melting behavior of boron nanoparticles with ab initio accuracy. We first validate the accuracy of the NNP comprehensively against DFT results via the prediction of atomic energy and force, crystallographic parameters, equation of state, elastic constant, phonon dispersion relationship, radial distribution function, as well as atomic thermal motion.…”

Section: Introductionmentioning

confidence: 99%