203 citations · 229 across the 12 of their papers we have counts for
12 papers
Fast and Accurate Equivariant Foundation Models for Atomistic Simulation
Seán R. Kavanagh, Chuin Wei Tan, Menghang Wang +12
Machine-learned interatomic potentials (MLIPs) have emerged as a transformative tool for computational materials science and chemistry, with universal potentials trained on large a…
Multiscale light-matter dynamics in quantum materials: from electrons to topological superlattices
Taufeq Mohammed Razakh, Thomas Linker, Ye Luo +12
Light-matter dynamics in topological quantum materials enables ultralow-power, ultrafast devices. A challenge is simulating multiple field and particle equations for light, electro…
High-performance training and inference for deep equivariant interatomic potentials
Chuin Wei Tan, Marc L. Descoteaux, Mit Kotak +11
Machine learning interatomic potentials, particularly those based on deep equivariant neural networks, have demonstrated state-of-the-art accuracy and computational efficiency in a…
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…
Atomistic simulations of out-of-equilibrium quantum nuclear dynamics
Francesco Libbi, Anders Johansson, Lorenzo Monacelli +1
The rapid advancements in ultrafast laser technology have paved the way for pumping and probing the out-of-equilibrium dynamics of nuclei in crystals. However, interpreting these e…
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…
Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials
Zachary A. H. Goodwin, Malia B. Wenny, Julia H. Yang +11
Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from energy storage to solvents, where they have been touted as ``designer solvents'' a…
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,…
Learning Interatomic Potentials at Multiple Scales
Xiang Fu, Albert Musaelian, Anders Johansson +2
The need to use a short time step is a key limit on the speed of molecular dynamics (MD) simulations. Simulations governed by classical potentials are often accelerated by using a…