179 citations · 362 across the 6 of their papers we have counts for
6 papers
Generalizability of Functional Forms for Interatomic Potential Models Discovered by Symbolic Regression
Alberto Hernandez, Tim Mueller
In recent years there has been great progress in the use of machine learning algorithms to develop interatomic potential models. Machine-learned potential models are typically orde…
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…
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…
Fast, accurate, and transferable many-body interatomic potentials by symbolic regression
Alberto Hernandez, Adarsh Balasubramanian, Fenglin Yuan +2
The length and time scales of atomistic simulations are limited by the computational cost of the methods used to predict material properties. In recent years there has been great p…
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…
Structural Disorder and Electronic Structure in Alloyed SrTiO3/SrFeO2.5 Compounds: A Theoretical Study
Bin Ouyang, Tim Mueller, Nicola H. Perry +2
Many mixed ionic/electronic conductors (MIECs) applied in fuel cell electrodes can be considered as alloys between perovskite oxides and ordered oxygen vacancy compounds. For examp…