203 citations · 229 across the 12 of their papers we have counts for
6 papers · 1 filter
A practical guide to machine learning interatomic potentials -- Status and future
Ryan Jacobs, Dane Morgan, Siamak Attarian +27
The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not exper…
Ultrafast quantum dynamics in under impulsive THz radiation
Francesco Libbi, Anders Johansson, Boris Kozinsky +1
Ultrafast spectroscopy paved the way for probing transient states of matter produced through photoexcitation. Despite significant advances, the microscopic processes governing the…
Atomistic evolution of active sites in multi-component heterogeneous catalysts
Cameron J. Owen, Lorenzo Russotto, Christopher R. O'Connor +4
Multi-component metal nanoparticles (NPs) are of paramount importance in the chemical industry, as most processes therein employ heterogeneous catalysts. While these multi-componen…
Unified Differentiable Learning of Electric Response
Stefano Falletta, Andrea Cepellotti, Anders Johansson +4
Predicting response of materials to external stimuli is a primary objective of computational materials science. However, current methods are limited to small-scale simulations due…
Phase discovery with active learning: Application to structural phase transitions in equiatomic NiTi
Jonathan Vandermause, Anders Johansson, Yucong Miao +2
Nickel titanium (NiTi) is a protypical shape-memory alloy used in a range of biomedical and engineering devices, but direct molecular dynamics simulations of the martensitic B19' -…
Unbiased Atomistic Predictions of Crystal Dislocation Dynamics using Bayesian Force Fields
Cameron J. Owen, Amirhossein D. Naghdi, Anders Johansson +3
Crystal dislocation dynamics, especially at high temperatures, represents a subject where experimental phenomenological input is commonly required, and parameter-free predictions,…