18 citations · 49 across the 6 of their papers we have counts for
7 papers
PASS: Perturbation augmented space group structure sampling for transferable Fe-O machine learning interatomic potential
Zixiong Wei, Fei Shuang, Poulumi Dey
Iron (Fe) oxidation is a fundamental phenomenon that is scientifically vital and technologically significant. Despite substantial experimental efforts, accurate atomistic modelling…
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…
Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys
Fei Shuang, Zixiong Wei, Kai Liu +2
Recent advances in machine learning, combined with the generation of extensive density functional theory (DFT) datasets, have enabled the development of universal machine learning…
Modeling Extensive Defects in Metals through Classical Potential-Guided Sampling and Automated Configuration Reconstruction
Fei Shuang, Kai Liu, Yucheng Ji +3
Extended defects such as dislocation networks and general grain boundaries are ubiquitous in metals, and accurately modeling these extensive defects is crucial for understanding th…
Decoding the hidden dynamics of super-Arrhenius hydrogen diffusion in multi-principal element alloys via machine learning
Fei Shuang, Yucheng Ji, Zixiong Wei +4
Understanding atomic hydrogen (H) diffusion in multi-principal element alloys (MPEAs) is essential for advancing clean energy technologies such as H transport, storage, and nuclear…