18 citations · 49 across the 7 of their papers we have counts for
4 papers · 1 filter
GUIDe: Generative and Uncertainty-Informed Inverse Design for On-Demand Nonlinear Functional Responses
Haoxuan Dylan Mu, Mingjian Tang, Wei Gao +1
Inverse design is a common yet challenging engineering problem, particularly for nonlinear functional responses such as mechanical behavior or spectral analysis. Deep generative mo…
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…