5 papers
Better without U: Impact of Selective Hubbard U Correction on Foundational MLIPs
Thomas Warford, Fabian L. Thiemann, Gábor Csányi
The training of foundational machine learning interatomic potentials (fMLIPs) relies on diverse databases with energies and forces calculated using ab initio methods. We show that…
Equivariant Interatomic Potentials without Tensor Products
Thiago Reschützegger, Sarp Aykent, Gabriel Jacob Perin +5
Foundational machine-learned interatomic potentials have emerged as powerful tools for atomistic simulations, promising near first-principles accuracy across diverse chemical space…
Computational tuning of the elastic properties of low- and high-entropy ultra-high temperature ceramics
Samuel J. Magorrian, Ljiljana Stojanović, Lara Kabalan +4
Ultra-high temperature ceramics (UHTCs) represent a class of crystalline materials for extreme environments. They can withstand extremely high temperatures but are mechanically dif…
Global properties of the energy landscape: a testing and training arena for machine learned potentials
Vlad Cărare, Fabian L. Thiemann, Joe Morrow +3
Machine learning interatomic potentials (MLIPs) have achieved remarkable accuracy on standard benchmarks, yet their ability to reproduce molecular kinetics -- critical for reaction…
Force-Free Molecular Dynamics Through Autoregressive Equivariant Networks
Fabian L. Thiemann, Thiago Reschützegger, Massimiliano Esposito +3
Molecular dynamics (MD) simulations play a crucial role in scientific research. Yet their computational cost often limits the timescales and system sizes that can be explored. Most…