17 citations · 32 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
Accurate atomistic modelling of iron (Fe) oxidation requires a reliable interatomic potential, which necessitates an extensive and representative first-principles dataset for train…
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…
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…
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…
Machine Learning Potential Powered Insights into the Mechanical Stability of Amorphous Li-Si Alloys
Zixiong Wei, Nongnuch Artrith
Understanding the mechanical properties of solid-state materials at the atomic scale is crucial for developing novel materials. For example, amorphous LiSi alloys are attractive an…