activity
20242026
most citedNEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements

19 citations · 19 across the 1 of their papers we have counts for

collaborators

7 papers

cond-mat.mtrl-sci202619 cited

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

Ting Liang, Ke Xu, Eric Lindgren +16

While machine-learned interatomic potentials offer near-quantum-mechanical accuracy for atomistic simulations, many are material-specific or computationally intensive, limiting the…

physics.comp-ph2026

NEP-CG and NEP-AACG: Efficient coarse-grained and multiscale all-atom-coarse-grained neuroevolution potentials

Zheyong Fan, Wenjun Zhang, Zhenhao Zhang +3

Machine-learned coarse-grained (CG) models often suffer from noisy training data, limiting their accuracy and transferability. We propose a method to generate low-noise training da…

physics.comp-ph2026

qNEP: A highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulations

Zheyong Fan, Benrui Tang, Esmée Berger +13

Although electrostatics can be incorporated into machine-learned interatomic potentials, existing approaches are computationally very demanding, limiting large-scale, long-time sim…

physics.chem-ph2025

Thermal conductivities of monolayer graphene oxide from machine learning molecular dynamics simulations

Bohan Zhang, Biyuan Liu, Penghua Ying +6

Graphene oxide (GO) exhibits rich chemical heterogeneity that strongly influences its structural, thermal, and mechanical properties, yet quantitatively linking reduction chemistry…

cond-mat.mtrl-sci2025

Anisotropic and isotropic elasticity and thermal transport in monolayer C networks from machine-learning molecular dynamics

Qing Li, Haikuan Dong, Penghua Ying +1

Two-dimensional fullerene networks have recently attracted increasing interest due to their diverse bonding topologies and mechanically robust architectures. In this work, we devel…

cond-mat.mes-hall2025

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

Yang Xiao, Yuqi Liu, Zihan Tan Bohan Zhang +5

Thermoelectric materials, which can convert waste heat to electricity or be utilized as solid-state coolers, hold promise for sustainable energy applications. However, optimizing t…