4 citations · 6 across the 3 of their papers we have counts for
4 papers
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…
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…
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 $…
Parameterizing Intersecting Surfaces via Invariants
Timon S. Gutleb, Rhyan Barrett, Julia Westermayr +1
We introduce and analyze numerical companion matrix methods for the reconstruction of hypersurfaces with crossings from smooth interpolants given unordered or, without loss of gene…