From the 1 of 4 linked papers with an AI index.
4 papers
Melt-Quench Failures and Practical Solutions for Universal Machine-Learning Interatomic Potentials in Amorphous Structure Generation
Shuwei Li, Yuqi An, Xingyu Guo +2
The paper investigates why universal machine‑learning interatomic potentials (uMLIPs) often produce unrealistically low densities when used for melt‑quench simulations of amorphous…
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…