19 citations · 19 across the 1 of their papers we have counts for
18 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…
Structurally Triggered Breakdown of the Phonon Gas Model in Crystalline Metal-Organic Frameworks
Penghua Ying, Ting Liang, Yun Chen +5
While crystalline materials with glass-like thermal conductivity are fundamentally intriguing, structurally triggering the transition from propagating to diffusive heat transport w…
Heat transport in superionic materials via machine-learned molecular dynamics
Wenjiang Zhou, Benrui Tang, Zheyong Fan +3
Precise modeling and understanding of heat transport in the superionic phase are of great interest. Although simulations combining Green-Kubo (GK) molecular dynamics with machine-l…
Modular hybrid machine learning and physics-based potentials for scalable modeling of van der Waals heterostructures
Hekai Bu, Wenwu Jiang, Penghua Ying +3
Accurately modeling the structural reconstruction and thermodynamic behavior of van der Waals (vdW) heterostructures remains a significant challenge due to the limitations of conve…
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…
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…