The thermoelectric performance of new structure SnSe studied by quotient graph and deep learning potential

Guo, Donglin; Li, C.; Li, K.; Shao, Bin; Chen, D.; Ma, Yilong; Sun, Jianchun; Cao, Xianlong; Zeng, Wen; Chang, Xueli

doi:10.1016/j.mtener.2021.100665

Cited by 14 publications

(11 citation statements)

References 35 publications

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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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Section: Multi-element Bulk Systemsmentioning

confidence: 99%

Deep potentials for materials science

Wen

Zhang²,

Wang

et al. 2022

Mater. Futures

View full text Add to dashboard Cite

show abstract

“…To generate enough structure configurations of a hydrogenated biphenylene sheet, its 2 × 2 supercell (containing 24 carbon atoms) is considered as the start point. Hydrogen atoms are randomly bonded to carbon atoms in the supercell through our previously developed RG 2 code, − which is designed for generating crystal structures with a well-defined structural feature. − For each carbon atom in the supercell, there are two different hydrogenating manners, namely, up (U) and down (D). The configuration space can be easily evaluated as 2 24 = 16 777 216, but most of them are distorted with high energy and some of them are equivalent due to symmetry.…”

mentioning

confidence: 99%