3 papers
physics.comp-ph2026
Scalable Data-Driven Basis Selection for Linear Machine Learning Interatomic Potentials
Tina Torabi, Matthias Militzer, Michael P. Friedlander +1
Machine learning interatomic potentials (MLIPs) provide an effective approach for accurately and efficiently modeling atomic interactions, expanding the capabilities of atomistic s…
physics.chem-ph2025
Transferable Machine Learning Potential X-MACE for Excited States using Integrated DeepSets
Rhyan Barrett, Christoph Ortner, Julia Westermayr
Conical intersections serve as critical gateways in photochemical reactions, enabling rapid nonradiative transitions between potential energy surfaces that underpin fundamental pro…
physics.comp-ph2025
Fast automatically differentiable matrix functions and applications in molecular simulations
Tina Torabi, Timon S Gutleb, Christoph Ortner
We describe efficient differentiation methods for computing Jacobians and gradients of a large class of matrix functions including the matrix logarithm and -th roots $…