amorphous structure generation 1density prediction 1machine learning interatomic potentials 1melt-quench simulations 1validation protocols 1
From the 1 of 2 linked papers with an AI index.
2 papers
cond-mat.mtrl-sci2026
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…
cond-mat.mtrl-sci2026
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…