activity
20092021
most citedCompressive sensing as a new paradigm for model building

238 citations · 283 across the 7 of their papers we have counts for

collaborators

14 papers

cond-mat.mtrl-sci202132 cited

Effectiveness of smearing and tetrahedron methods: best practices in DFT codes

Jeremy J. Jorgensen, Gus L. W. Hart

Density functional theory (DFT) codes are commonly treated as a "black box" in high-throughput screening of materials, with users opting for the default values of the input paramet…

cond-mat.mtrl-sci2020

The AFLOW Library of Crystallographic Prototypes: Part 3

David Hicks, Michael J. Mehl, Marco Esters +5

The AFLOW Library of Crystallographic Prototypes has been extended to include a total of 1,100 common crystal structural prototypes (510 new ones with Part 3), comprising all of th…

cond-mat.mtrl-sci20192 cited

Machine-learned Interatomic Potentials for Alloys and Alloy Phase Diagrams

Conrad W. Rosenbrock, Konstantin Gubaev, Alexander V. Shapeev +4

We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of compositions. We compare two different approaches. Moment tens…

cond-mat.mtrl-sci20191 cited

Generalized Regular k-point Grid Generation On The Fly

Wiley S. Morgan, John E. Christensen, Parker K. Hamilton +4

In the DFT community, it is common practice to use regular k-point grids (Monkhorst-Pack, MP) for Brillioun zone integration. Recently Wisesa et. al.\cite{wisesa2016efficient} and…

physics.comp-ph2018

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…

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…