Shunda Chen

George Washington University — United States

Materials Science · Physical Sciences

18h-index1.0kcitations87works10.02yr avg
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Research Topics

Thermal properties of materials(31), Machine Learning in Materials Science(25), Advanced Thermoelectric Materials and Devices(15), Semiconductor materials and interfaces(9), Crystallization and Solubility Studies(9)

Publications87 total

qNEP: A highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulations
arXiv (Cornell University)·2026Open Access
qNEP: A highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulations
ArXiv.org·2026Open Access
Data and models supporting "qNEP: A highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulations"
Zenodo (CERN European Organization for Nuclear Research)·2026Open Access
Data and models supporting "qNEP: A highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulations"
Zenodo (CERN European Organization for Nuclear Research)·2026Open Access
Identification of short-range ordering motifs in semiconductors
Science·2025
NEP-MB-pol: a unified machine-learned framework for fast and accurate prediction of water’s thermodynamic and transport properties
npj Computational Materials·2025· 6 citedOpen Access
Optimizing thermoelectric performance of graphene antidot lattices via quantum transport and machine-learning molecular dynamics simulations
Physical Review Materials·2025· 3 cited
NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements
2025· 2 citedOpen Access
GPUMD 4.0: A high‐performance molecular dynamics package for versatile materials simulations with machine‐learned potentials
Materials Genome Engineering Advances·2025· 38 citedOpen Access
Metastability and Ostwald step rule in the crystallisation of diamond and graphite from molten carbon
Nature Communications·2025· 5 citedOpen Access
NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements
ArXiv.org·2025· 2 citedOpen Access
Optimizing thermoelectric performance of graphene antidot lattices via quantum transport and machine-learning molecular dynamics simulations
arXiv (Cornell University)·2025· 1 citedOpen Access
Semiconductor-compatible topological digital alloys
Materials Today·2025· 3 cited
NEP-MB-pol: A Unified Machine-Learned Framework for Fast and Accurate Prediction of Water's Thermodynamic and Transport Properties
2025· 1 citedOpen Access
Metastability and Ostwald Step Rule in the Crystallisation of Diamond and Graphite from Molten Carbon
arXiv (Cornell University)·2025Open Access
Metastability and Ostwald Step Rule in the Crystallisation of Diamond and Graphite from Molten Carbon
2025· 1 citedOpen Access
Publisher's Note: “Advances in modeling complex materials: The rise of neuroevolution potentials” [Chem. Phys. Rev. <b>6</b>, 011310 (2025)]
Chemical Physics Reviews·2025· 1 citedOpen Access
Advances in modeling complex materials: The rise of neuroevolution potentials
Chemical Physics Reviews·2025· 25 citedOpen Access
Semiconductor-compatible topological digital alloys
arXiv (Cornell University)·2025Open Access
Highly efficient path-integral molecular dynamics simulations with GPUMD using neuroevolution potentials: Case studies on thermal properties of materials
The Journal of Chemical Physics·2025· 18 cited
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Frequent Co-authors

Zheyong Fan(18), Tianshu Li(14), Penghua Ying(13), Haikuan Dong(13), Ke Xu(11), Davide Donadio(10), Xiaochen Jin(9), Yanzhou Wang(8), Keke Song(7), Jiahui Liu(7), Paul Erhart(7), Jiao Wang(7), Eric Lindgren(6), Ting Liang(6), Tapio Ala-Nissilä(6), Zezhu Zeng(5), Yanjing Su(5), Shiyun Xiong(5), Hong Zhao(5), Rui Zhao(4)