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Shunda Chen

Research Scientist

George Washington University

United States

Research Interests

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Articles (20)

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2026

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Collaborators (11)

Shuiqing Yu

Professor

University of Arkansas

UNITED STATES

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Yanjing Su

University of Science and Technology Beijing

CHINA

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Erik Fransson

Chalmers tekniska högskola

SWEDEN

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Dragica Vasileska

Professor

Arizona State University

UNITED STATES

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Tianshu Li

Professor

George Washington University

UNITED STATES

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Jifeng Liu

Professor

Dartmouth College

UNITED STATES

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Ting Liang

posdoc

The Chinese University of Hong Kong (CUHK)

HONG KONG

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Cheng Qian

Korea Advanced Institute of Science and Technology

SOUTH KOREA

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Julia Wiktor

Chalmers tekniska högskola

SWEDEN

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Lilian Vogl

W3 Professorship

Ulm University

GERMANY

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Andrew M. Minor

Professor

University of California

UNITED STATES

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Shunda Chen

Research Interests

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Articles (20)

qNEP: A Highly Efficient Neuroevolution Potential with Dynamic Charges for Large-Scale Atomistic Simulations

Unraveling Ordering Phenomena in Alloys using Advanced TEM Techniques

NEP89: universal neuroevolution potential for inorganic and organic materials across 89 elements

Solute Segregation in Polycrystalline Aluminum From Hybrid Monte Carlo and Molecular Dynamics Simulations With a Unified Neuroevolution Potential

Modeling and Simulation of Electrostatics of Ge$_{\text{1-x}}$Sn$_{\text{x}}$ Layers Grown on Ge Substrates

Highly efficient path-integral molecular dynamics simulations with GPUMD using neuroevolution potentials: Case studies on thermal properties of materials

Advances in modeling complex materials: The rise of neuroevolution potentials

Publisher's Note: “Advances in modeling complex materials: The rise of neuroevolution potentials” [Chem. Phys. Rev. 6, 011310 (2025)]

GPUMD 4.0: A high‐performance molecular dynamics package for versatile materials simulations with machine‐learned potentials

Identification of short-range ordering motifs in semiconductors

Optimizing thermoelectric performance of graphene antidot lattices via quantum transport and machine-learning molecular dynamics simulations

Metastability and Ostwald step rule in the crystallisation of diamond and graphite from molten carbon

Million-atom heat transport simulations of polycrystalline graphene approaching first-principles accuracy enabled by neuroevolution potential on desktop GPUs

Enabling Type I Lattice-Matched Heterostructures in SiGeSn Alloys Through Engineering Composition and Short-Range Order: A First-Principles Perspective

NEP-MB-pol: a unified machine-learned framework for fast and accurate prediction of water's thermodynamic and transport properties

Semiconductor-compatible topological digital alloys

General-purpose machine-learned potential for 16 elemental metals and their alloys

(Invited) Raman Spectroscopy for Epitaxial Silicon-Germanium-Tin Alloys

Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials

Intricate short-range order in GeSn alloys revealed by atomistic simulations with highly accurate and efficient machine-learning potentials

Collaborators (11)

Shuiqing Yu

Yanjing Su

Erik Fransson

Dragica Vasileska

Tianshu Li

Jifeng Liu

Ting Liang

Cheng Qian

Julia Wiktor

Lilian Vogl

Andrew M. Minor

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