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cond-mat.mtrl-sci2025
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials
Fei Shuang, Zixiong Wei, Kai Liu +2
Machine learning interatomic potentials (MLIPs) enable accurate atomistic modelling, but reliable uncertainty quantification (UQ) remains elusive. In this study, we investigate two…
cond-mat.mtrl-sci2025
Heterogeneous Ensemble Enables a Universal Uncertainty Metric for Atomistic Foundation Models
Kai Liu, Zixiong Wei, Wei Gao +3
Universal machine learning interatomic potentials (uMLIPs) are reshaping atomistic simulation as foundation models, delivering near \textit{ab initio} accuracy at a fraction of the…
cond-mat.mtrl-sci2025
Kinetics of Vacancy-Assisted Reversible Phase Transition in Monolayer MoTe
Fei Shuang, Daniel Ocampo, Reza Namakian +3
We investigate the kinetics of phase transition between the 2H and 1T phases in monolayer MoTe using atomistic simulations based on a machine learning interatomic pote…