Chenxing Luo

Columbia University — United States

Earth and Planetary Sciences · Physical Sciences

7h-index620citations32works4.92yr avg
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Research Topics

High-pressure geophysics and materials(12), Machine Learning in Materials Science(8), X-ray Diffraction in Crystallography(5), earthquake and tectonic studies(4), Geological and Geochemical Analysis(3)

Publications32 total

Deep Learning Potential for Hydrous Stishovite and Post-Stishovite (Si-O-H System) based on r²SCAN meta-GGA
Zenodo (CERN European Organization for Nuclear Research)·2026Open Access
Deep Learning Potential for Hydrous Stishovite and Post-Stishovite (Si-O-H System) based on r²SCAN meta-GGA
Zenodo (CERN European Organization for Nuclear Research)·2026Open Access
lnnbig/deepmd-kit2.2.10: deepmd_v2.2.10
Zenodo (CERN European Organization for Nuclear Research)·2026Open Access
lnnbig/deepmd-kit2.2.10: deepmd_v2.2.10
Zenodo (CERN European Organization for Nuclear Research)·2026Open Access
dpdata: A Scalable Python Toolkit for Atomistic Machine Learning Data Sets
Journal of Chemical Information and Modeling·2025· 1 cited
Ferroelasticity, shear modulus softening, and the tetragonal-cubic transition in davemaoite
arXiv (Cornell University)·2025Open Access
DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials
Journal of Chemical Theory and Computation·2025· 46 citedOpen Access
Machine Learning Potential for Serpentines
Journal of Geophysical Research Machine Learning and Computation·2024· 1 citedOpen Access
Elasticity and acoustic velocities of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>δ</mml:mi><mml:mtext>−</mml:mtext><mml:mi>AlOOH</mml:mi></mml:mrow></mml:math> at extreme conditions: A methodology assessment
Physical Review Materials·2024· 2 citedOpen Access
Machine learning potential for serpentines
2024· 1 cited
Machine learning potential for serpentines
arXiv (Cornell University)·2024Open Access
Elasticity and acoustic velocities of $δ$-AlOOH at extreme conditions: a methodology assessment
arXiv (Cornell University)·2024Open Access
<i>Ab initio</i> study of the stability and elasticity of brucite
Physical review. B./Physical review. B·2024· 5 citedOpen Access
Elasticity of Hydrous SiO<sub>2</sub> Across the Post‐Stishovite Transition in the Lower Mantle
Geophysical Research Letters·2024· 5 citedOpen Access
Probing the state of hydrogen in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>δ</mml:mi><mml:mtext>−</mml:mtext><mml:mi>AlOOH</mml:mi></mml:math> at mantle conditions with machine learning potential
Physical Review Research·2024· 7 citedOpen Access
Thermoelastic properties of bridgmanite using deep-potential molecular dynamics
Physical review. B./Physical review. B·2024· 11 citedOpen Access
Ab initio study on the stability and elasticity of brucite
arXiv (Cornell University)·2023Open Access
Probing the state of hydrogen in $δ$-AlOOH at mantle conditions with machine learning potential
arXiv (Cornell University)·2023· 1 citedOpen Access
DeePMD-kit v2: A software package for deep potential models
The Journal of Chemical Physics·2023· 456 citedOpen Access
Thermoelastic properties of bridgmanite using Deep Potential Molecular Dynamics
arXiv (Cornell University)·2023· 1 citedOpen Access
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Frequent Co-authors

Renata M. Wentzcovitch(24), Jinzhe Zeng(6), Tianqi Wan(6), Yang Sun(5), Hongjin Wang(5), Duo Zhang(4), Yifan Li(4), Jiaxin Zhu(4), Linfeng Zhang(4), Handong Wang(4), Han Wang(4), Denghui Lu(3), Pinghui Mo(3), Yixiao Chen(3), Marián Rynik(3), Shaochen Shi(3), Yingze Wang(3), Ping Tuo(3), Ye Ding(3), Qiyu Zeng(3)