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20112024
most citedAccelerating crystal structure prediction by machine-learning interatomic potentials with active learning

403 citations

Showing 2018Show all

6 papers · 1 filter

cond-mat.mtrl-sci2018★ 18 cited

Mechanical relaxation and fracture of phase field crystals

Wenquan Zhou, Jincheng Wang, Zhijun Wang +1

A computational method is developed for the study of mechanical response and fracture behavior of phase field crystals (PFC), to overcome a limitation of the PFC dynamics which lac…

cond-mat.mtrl-sci2018★ 43 cited

Ternary Inorganic Electrides with mixed bonding

Junjie Wang, Qiang Zhu, Zhenhai Wang +1

A high-throughput screening based on first-principles calculations was performed to search for new ternary inorganic electrides. From the available materials database, we identifie…

cond-mat.mtrl-sci2018★ 69 cited

Towards Air Stability of Ultra-Thin GaSe Devices: Avoiding Environmental and Laser-Induced Degradation by Encapsulation

Qinghua Zhao, Riccardo Frisenda, Patricia Gant +7

Gallium selenide (GaSe) is a novel two-dimensional material, which belongs to the layered III-VIA semiconductors family and attracted interest recently as it displays single-photon…

physics.optics2018

Multiple optical frequency-conversions in few-layer GaSe assisted by a photonic crystal cavity

Liang Fang, Qingchen Yuan, Hanlin Fang +6

While two-dimensional (2D) materials have intriguing second-order nonlinearities with ultrahigh coefficient and electrical tunability, their atomic layer thicknesses hinder explora…

cond-mat.mes-hall2018★ 78 cited

Experimental Observation of Acoustic Weyl Points and Topological Surface States

Hao Ge, Xu Ni, Yuan Tian +6

Weyl points emerge as topological monopoles of Berry flux in the three-dimensional (3D) momentum space and have been extensively studied in topological semimetals. As the underlyin…

cond-mat.mtrl-sci2018★ 403 cited

Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning

Evgeny V. Podryabinkin, Evgeny V. Tikhonov, Alexander V. Shapeev +1

In this letter we propose a new methodology for crystal structure prediction, which is based on the evolutionary algorithm USPEX and the machine-learning interatomic potentials act…