1 citations · 1 across the 1 of their papers we have counts for
4 papers
Spin-adapted neural network backflow for symmetry-preserving simulations of strongly correlated electrons
Yunzhi Li, Zibo Wu, Bohan Zhang +2
Strongly correlated molecules often contain dense manifolds of low-lying spin states, making total-spin symmetry essential for predictive electronic-structure theory. Neural-networ…
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…
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…
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…