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20192021
most citedA Perspective on Deep Learning for Molecular Modeling and Simulations

57 citations

Showing 2019Show all

5 papers · 1 filter

cond-mat.quant-gas2019★ 9 cited

Topological defects of spin-orbit coupled Bose-Einstein condensates in a rotating anharmonic trap

Chunxiao Shi, Linghua Wen, Qingbo Wang +2

We investigate the topological defects and spin structures of binary Bose-Einstein condensates (BECs) with Dresselhaus spin-orbit coupling (D-SOC) in a rotating anharmonic trap. Ou…

cond-mat.quant-gas2019★ 6 cited

Ground states of dipolar spin-orbit-coupled Bose-Einstein condensates in a toroidal trap

Hui Yang, Qingbo Wang, Linghua Wen

We investigate the ground-state structures of dipolar spin-orbit-coupled Bose-Einstein condensates in a toroidal trap. Combined effects of dipole-dipole interaction (DDI) and spin-…

cond-mat.quant-gas2019★ 20 cited

Topological excitations in rotating Bose-Einstein condensates with Rashba-Dresselhaus spin-orbit coupling in a two-dimensional optical lattice

Hui Yang, Qingbo Wang, Ning Su +1

We study the ground-state configurations and spin textures of rotating two-component Bose-Einstein condensates (BECs) with Rashba-Dresselhaus spin-orbit coupling (RD-SOC), which ar…

cond-mat.quant-gas2019★ 9 cited

Ground-state properties of spin-orbit-coupled dipolar Bose-Einstein condensates with in-plane gradient magnetic field

Xiaoqian Li, Qingbo Wang, Huan Wang +3

We investigate the ground-state properties of spin-orbit-coupled pseudo-spin-1/2 dipolar Bose-Einstein condensates (BECs) in a two-dimensional harmonic trap and an in-plane quadrup…

cond-mat.stat-mech2019★ 21 cited

Learning Clustered Representation for Complex Free Energy Landscapes

Jun Zhang, Yao-Kun Lei, Xing Che +3

In this paper we first analyzed the inductive bias underlying the data scattered across complex free energy landscapes (FEL), and exploited it to train deep neural networks which y…