533 citations · 567 across the 11 of their papers we have counts for
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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…
Iterative charge equilibration for fourth-generation high-dimensional neural network potentials
Emir Kocer, Andreas Singraber, Jonas A. Finkler +4
Machine learning potentials (MLP) allow to perform large-scale molecular dynamics simulations with about the same accuracy as electronic structure calculations provided that the se…
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…