268 citations · 362 across the 8 of their papers we have counts for
4 papers · 1 filter
Melt-Quench Failures and Practical Solutions for Universal Machine-Learning Interatomic Potentials in Amorphous Structure Generation
Shuwei Li, Yuqi An, Xingyu Guo +2
Generating experimentally relevant amorphous structures via melt-quench molecular dynamics is prohibitively expensive at the first-principles level. Universal machine-learning inte…
Predicting Novel Stable Materials for Experimental Synthesis
Yuqi An, Sihong Zhu, Joseph Montoya +2
Machine-learning-accelerated materials discovery has yielded large numbers of computationally stable compounds, yet many remain experimentally unrealized, underscoring a persistent…
Accelerating Complex Materials Discovery with Universal Machine-Learning Potential-Driven Structure Prediction
Yuqi An, Zhenbin Wang
Universal machine-learning interatomic potentials (uMLIPs) have become powerful tools for accelerating computational materials discovery by replacing expensive first-principles cal…
Are Universal Potentials Ready for Alkali-Ion Battery Kinetics?
Xingyu Guo, Cheng Gui, Zhenbin Wang
Accelerating alkali-ion battery discovery requires accurate modeling of atomic-scale kinetics, yet the reliability of universal machine learning interatomic potentials (uMLIPs) in…