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Npj Computational Materials|March 16, 2026
Efficient and accurate spatial mixing of machine learned interatomic potentials for materials scienceFraser Birks, Matthew Nutter, Thomas D Swinburne, et al.Physical Review Letters|December 22, 2023
Coarse-Graining and Forecasting Atomic Material Simulations with DescriptorsThomas D SwinburneJournal of Physics. Condensed Matter : an Institute of Physics Journal|March 27, 2020
f90wrap: an automated tool for constructing deep Python interfaces to modern Fortran codesJames R KermodePhysical Review. E|April 17, 2021
Numerical-continuation-enhanced flexible boundary condition scheme applied to mode-I and mode-III fractureMaciej Buze, James R KermodePhysical Review Letters|April 26, 2018
Unsupervised Calculation of Free Energy Barriers in Large Crystalline SystemsThomas D Swinburne, Mihai-Cosmin MarinicaJournal of Chemical Theory and Computation|March 11, 2020
Defining, Calculating, and Converging Observables of a Kinetic Transition NetworkThomas D Swinburne, David J WalesThe Journal of Chemical Physics|March 10, 2019
A preconditioning scheme for minimum energy path finding methodsStela Makri, Christoph Ortner, James R KermodeThe Journal of Chemical Physics|October 22, 2020
Sensitivity and dimensionality of atomic environment representations used for machine learning interatomic potentialsBerk Onat, Christoph Ortner, James R KermodeNature Communications|December 7, 2025
Score matching the descriptor density of states for model-agnostic free energy estimationThomas D Swinburne, Clovis Lapointe, Mihai-Cosmin MarinicaPhysical Review Letters|March 21, 2015
Molecular dynamics with on-the-fly machine learning of quantum-mechanical forcesZhenwei Li, James R Kermode, Alessandro De VitaPageof 4