Chenxing Luo
Columbia University
7 Papers
2 Citations
Chenxing Luo is an academic researcher from Columbia University. The author has contributed to research in topics: Density functional theory & Personal computer. The author has co-authored 1 publications.
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Papers
DeePMD-kit v2: A software package for Deep Potential models
Jinzhe Zeng,Duoduo Zhang,Denghui Lu,Pinghui Mo,Yixiao Chen,Mari'an Ryn'ik,Liang Huang,Zi Tong Li,Shaochen Shi,Yingze Wang,Hao-Tong Ye,Ping Tuo,Ye Ding,Yifan Li,D. Tisi,Qiyu Zeng,Yu Xia,Koki Muraoka,Junhan Chang,Feng Yuan,Sigbjørn Løland Bore,Chun-Lin Cai,Yinnian Lin,Bo Wang,Jia-yu Xu,Jiahong Zhu,Chenxing Luo,Yuzhi Zhang,Rhys E. A. Goodall,Wenshuo Liang,Sikai Yao,Jingchao Zhang,Renata M. Wentzcovitch,Jiequn Han,Jieming Liu,Wei Jia,Darrin M. York,E Weinan,Roberto Car,Linfeng Zhang,Han Wang +40 more
- 19 Apr 2023
TL;DR: The DeePMD-kit as mentioned in this paper is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models.
cij: A Python code for quasiharmonic thermoelasticity
Chenxing Luo,Xin Deng,Wenzhong Wang,Wenzhong Wang,Gaurav Shukla,Zhongqing Wu,Zhongqing Wu,Renata M. Wentzcovitch,Renata M. Wentzcovitch +8 more
TL;DR: Wu and Wentzcovitch as mentioned in this paper proposed the cij package, a Python implementation of the SAM-Cij formalism that enables a thermoelasticity calculation to be initiated from a single command and fully configurable from a calculation settings file to work with solids within any crystalline system.
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Elastic Anisotropy of Lizardite at Subduction Zone Conditions
TL;DR: In this article , the elasticity and acoustic wave velocities of lizardite at P•T conditions of subduction zones were reported. But the authors did not investigate the elastic anisotropy of dragonite.
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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials.
Jinzhe Zeng,Duo Zhang,Anyang Peng,Xiangyu Zhang,Sensen He,Yan Wang,Xinzijian Liu,Hangrui Bi,Yifan Li,Chun Cai,Chengqian Zhang,Yiming Du,Jiahong Zhu,Pinghui Mo,Zhengtao Huang,Qiyu Zeng,Shaochen Shi,Xu-Tan Qin,Zhaoxi Yu,Chenxing Luo,Ye Ding,Yunchuan Liu,Ruosong Shi,Zhenyu Wang,Sigbjørn Løland Bore,Junhan Chang,Zhenghua Deng,Zhaohan Ding,Siyuan Han,Wanrun Jiang,Guolin Ke,Zhaoqing Liu,D. Lu,Koki Muraoka,H. Oliaei,Anurag Kumar Singh,Haohui Que,Weihong Xu,Zhangmancang Xu,Yong-Bin Zhuang,Jiayu Dai,Timothy J. Giese,Weile Jia,B. Xu,Darrin M. York,Linfeng Zhang,Han Wang +46 more
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Probing the state of hydrogen in δ−AlOOH at mantle conditions with machine learning potential
Chenxing Luo,Yang Sun,Renata M. Wentzcovitch +2 more
TL;DR: A hybrid approach that combines deep learning potentials (DP) with the SCAN meta-GGA functional to simulate a prototypical hydrous system is adopted, and a high-throughput sampling of phase space using molecular dynamics simulations with DP-potentials sheds light on the hydrogen-bond behavior and proton diffusion at geophysical conditions.
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