From the 1 of 7 linked papers with an AI index.
7 papers
Girsanov Reweighting for Uncertainty Propagation in Rare-Event Kinetics
Leonard Moracchini, Thomas Pigeon, Morgane Menz +3
The paper presents a method that uses Girsanov reweighting to propagate uncertainties from machine‑learning interatomic potentials to kinetic observables like the committor probabi…
Finite Temperature Stacking Fault Stability in Random and Locally Ordered CoCrNi beyond the Harmonic Approximation
Reza Namakian, Fei Shuang, Thomas D Swinburne +3
Previous density functional theory (DFT) calculations for random solid solution (RSS) CoCrNi predict negative intrinsic stacking-fault energy (ISFE) at 0 K, contrary to experimenta…
Efficient and Accurate Spatial Mixing of Machine Learned Interatomic Potentials for Materials Science
Fraser Birks, Matthew Nutter, Thomas D Swinburne +1
Machine-learned interatomic potentials can offer near first-principles accuracy but are computationally expensive, limiting their application to large-scale molecular dynamics simu…
Activation entropy of dislocation glide in body-centered cubic metals from atomistic simulations
Arnaud Allera, Thomas D. Swinburne, Alexandra M. Goryaeva +5
The activation entropy of dislocation glide, a key process controlling the strength of many metals, is often assumed to be constant or linked to enthalpy through the empirical Meye…
Agnostic calculation of atomic free energies with the descriptor density of states
Thomas D Swinburne, Clovis Lapointe, Mihai-Cosmin Marinica
We present a new method to evaluate vibrational free energies of atomic systems without a priori specification of an interatomic potential. Our model-agnostic approach leverages de…
Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials
Danny Perez, Aparna P. A. Subramanyam, Ivan Maliyov +1
The use of high-dimensional regression techniques from machine learning has significantly improved the quantitative accuracy of interatomic potentials. Atomic simulations can now p…