3 papers
physics.chem-ph2020
Predicting activation energies for vacancy-mediated diffusion in alloys using a transition-state cluster expansion
Chenyang Li, Thomas Nilson, Liang Cao +1
Kinetic Monte Carlo models parameterized by first principles calculations are widely used to simulate atomic diffusion. However, accurately predicting the activation energies for d…
physics.comp-ph2019
Rapid Generation of Optimal Generalized Monkhorst-Pack Grids
Yunzhe Wang, Pandu Wisesa, Adarsh Balasubramanian +2
Computational modeling of the properties of crystalline materials has become an increasingly important aspect of materials research, consuming hundreds of millions of CPU-hours at…
cond-mat.mtrl-sci2018
Machine-learned multi-system surrogate models for materials prediction
Chandramouli Nyshadham, Matthias Rupp, Brayden Bekker +6
Surrogate machine-learning models are transforming computational materials science by predicting properties of materials with the accuracy of ab initio methods at a fraction of the…