3 papers
cond-mat.mtrl-sci2025
A Linear-Scaling, Charge-Aware Foundation Potential for Atomistic Simulations
Tsz Wai Ko, Runze Liu, Adesh Rohan Mishra +3
Electrostatics govern charge transfer and reactivity in materials. However, most foundation potentials (FPs) either neglect explicit electrostatic interactions or come at prohibiti…
cond-mat.mtrl-sci2025
A Foundational Potential Energy Surface Dataset for Materials
Aaron D. Kaplan, Runze Liu, Ji Qi +6
Accurate potential energy surface (PES) descriptions are essential for atomistic simulations of materials. Universal machine learning interatomic potentials (UMLIPs) offer…
cond-mat.mtrl-sci2025
Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry
Tsz Wai Ko, Bowen Deng, Marcel Nassar +7
Graph deep learning models, which incorporate a natural inductive bias for a collection of atoms, are of immense interest in materials science and chemistry. Here, we introduce the…