18 citations · 19 across the 6 of their papers we have counts for
7 papers · 1 filter
PASS: Perturbation augmented space group structure sampling for transferable Fe-O machine learning interatomic potential
Zixiong Wei, Fei Shuang, Poulumi Dey
Accurate atomistic modelling of iron (Fe) oxidation requires a reliable interatomic potential, which necessitates an extensive and representative first-principles dataset for train…
Finite Temperature Stacking Fault Stability in Random and Locally Ordered CoCrNi beyond the Harmonic Approximation
Reza Namakian, Fei Shuang, Thomas D Swinburne +3
Previous density functional theory (DFT) calculations for random solid solution (RSS) CoCrNi predict negative intrinsic stacking-fault energy (ISFE) at 0 K, contrary to experimenta…
Benchmarking Chemically Scalable Machine-Learning Interatomic Potentials for Large-Scale Simulations of Multicomponent Alloys
Fei Shuang, Penghua Ying, Kai Liu +5
Machine learning interatomic potentials (MLIPs) with broad chemical flexibility are essential for atomistic simulations of compositionally complex alloys, but their deployment in l…
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…
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…
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…