Kavli Affiliate: Kristin A. Persson | First 5 Authors: Aaron D. Kaplan, Runze Liu, Ji Qi, Tsz Wai Ko, Bowen Deng | Summary: Accurate potential energy surface (PES) descriptions are essential for atomistic simulations of materials. Universal machine learning interatomic potentials (UMLIPs)$^{1-3}$ offer a computationally efficient alternative to density functional theory (DFT)$^4$ for PES modeling […]
Continue.. A Foundational Potential Energy Surface Dataset for Materials