Evaluating mechanical property prediction across material classes using molecular dynamics simulations with universal machine-learned interatomic potentials (opens in new tab)

Simulating the mechanical and thermal properties of materials requires accurate treatment of interatomic interactions, yet quantum-mechanical methods can be computationally prohibitive for the time scales needed. Universal machine-learned interatomic potentials (MLIPs) offer a promising alternative, but their reliability for dynamics across diverse material classes remains largely untested. Here, we assess the accuracy of six universal MLIPs for predicting the temperature and pressure respons...