19 citations · 19 across the 1 of their papers we have counts for
6 papers
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…
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…
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…
Advances in modeling complex materials: The rise of neuroevolution potentials
Penghua Ying, Cheng Qian, Rui Zhao +4
Interatomic potentials are essential for driving molecular dynamics (MD) simulations, directly impacting the reliability of predictions regarding the physical and chemical properti…
NEP-MB-pol: A unified machine-learned framework for fast and accurate prediction of water's thermodynamic and transport properties
Ke Xu, Ting Liang, Nan Xu +5
Water's unique hydrogen-bonding network and anomalous properties pose significant challenges for accurately modeling its structural, thermodynamic, and transport behavior across va…
Million-atom heat transport simulations of polycrystalline graphene approaching first-principles accuracy enabled by neuroevolution potential on desktop GPUs
Xiaoye Zhou, Yuqi Liu, Benrui Tang +5
First-principles molecular dynamics simulations of heat transport in systems with large-scale structural features are challenging due to their high computational cost. Here, using…