1.6k citations · 4k across the 47 of their papers we have counts for
6 papers · 1 filter
A robust algorithm for -point grid generation and symmetry reduction
Gus L. W. Hart, Jeremy J. Jorgensen, Wiley S. Morgan +1
We develop an algorithm for i) computing generalized regular -point grids, ii) reducing the grids to their symmetrically distinct points, and iii) mapping the reduced grid point…
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 Characterization of Grain Boundaries and Machine Learning of Grain Boundary Energy and Mobility
Conrad W. Rosenbrock, Jonathan L. Priedeman, Gus L. W. Hart +1
Recent advances in the numerical representation of materials opened the way for successful machine learning of grain boundary (GB) energies and the classification of GB mobility an…
Accelerating high-throughput searches for new alloys with active learning of interatomic potentials
Konstantin Gubaev, Evgeny V. Podryabinkin, Gus L. W. Hart +1
We propose an approach to materials prediction that uses a machine-learning interatomic potential to approximate quantum-mechanical energies and an active learning algorithm for th…
The AFLOW Library of Crystallographic Prototypes: Part 2
David Hicks, Michael J. Mehl, Eric Gossett +5
Materials discovery via high-throughput methods relies on the availability of structural prototypes, which are generally decorated with varying combinations of elements to produce…
Efficiency of Generalized Regular k-point Grids
Wiley S. Morgan, Jeremy J. Jorgensen, Bret C. Hess +1
Most DFT practitioners use regular grids (Monkhorst-Pack, MP) for integrations in the Brillioun zone. Although regular grids are the natural choice and easy to generate, more gener…