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AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations

Computational materials science, 2012-06, Vol.58 (C), p.227-235

2012

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A computational high-throughput search for new ternary superalloys

Acta materialia, 2017-01, Vol.122, p.438-447

2017

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AFLOW: An automatic framework for high-throughput materials discovery

Computational materials science, 2012-06, Vol.58, p.218-226

2012

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Accelerating high-throughput searches for new alloys with active learning of interatomic potentials

Computational materials science, 2019-01, Vol.156, p.148-156

2019

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The high-throughput highway to computational materials design

Nature materials, 2013-03, Vol.12 (3), p.191-201

2013

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Examination of computed aluminum grain boundary structures and energies that span the 5D space of crystallographic character

Acta materialia, 2022-08, Vol.234, p.118006, Article 118006

2022

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Efficiency of Generalized Regular k-point grids

Computational materials science, 2018-10, Vol.153, p.424-430

2018

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Machine learning predictions of high-Curie-temperature materials

Applied physics letters, 2023-07, Vol.123 (4)

2023

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Hafnium binary alloys from experiments and first principles

Acta materialia, 2010-05, Vol.58 (8), p.2887-2897

2010

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The AFLOW Library of Crystallographic Prototypes: Part 3

Computational materials science, 2021-11, Vol.199, p.110450, Article 110450

2021

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Tensor-Reduced Atomic Density Representations

Physical review letters, 2023-07, Vol.131 (2), p.028001-028001, Article 028001

2023

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Machine-learned interatomic potentials for alloys and alloy phase diagrams

npj computational materials, 2021-01, Vol.7 (1), p.1-9, Article 24

2021

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

Modelling and simulation in materials science and engineering, 2021-09, Vol.29 (6), p.65014

2021

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Accelerating training of MLIPs through small-cell training

Journal of materials research, 2023-12, Vol.38 (24), p.5095-5105

2023

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Substitution with vision

Nature (London), 2012-11, Vol.486 (7426), p.674

Hart, Gus L.W

2012

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A Set of Moment Tensor Potentials for Zirconium with Increasing Complexity

Journal of chemical theory and computation, 2023-10, Vol.19 (19), p.6848-6856

2023

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Cluster expansion made easy with Bayesian compressive sensing

Physical review. B, Condensed matter and materials physics, 2013-10, Vol.88 (15), Article 155105

2013

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Comprehensive Search for New Phases and Compounds in Binary Alloy Systems Based on Platinum-Group Metals, Using a Computational First-Principles Approach

Physical review. X, 2013-12, Vol.3 (4), p.041035, Article 041035

2013

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Uncovering Compounds by Synergy of Cluster Expansion and High-Throughput Methods

Journal of the American Chemical Society, 2010-04, Vol.132 (13), p.4830-4833

2010

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Machine-learned multi-system surrogate models for materials prediction

npj computational materials, 2019-04, Vol.5 (1), Article 51

2019

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Generating derivative superstructures for systems with high configurational freedom

Computational materials science, 2017-08, Vol.136, p.144-149

2017

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Discovering the building blocks of atomic systems using machine learning: application to grain boundaries

npj computational materials, 2017-08, Vol.3 (1), p.1-7, Article 29

2017

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Generating derivative structures at a fixed concentration

Computational materials science, 2012-06, Vol.59, p.101-107

2012

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Evolutionary approach for determining first-principles hamiltonians

Nature materials, 2005-05, Vol.4 (5), p.391-394

2005

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Generalized regular k-point grid generation on the fly

Computational materials science, 2020-02, Vol.173, p.109340, Article 109340

2020

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AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations

A computational high-throughput search for new ternary superalloys

AFLOW: An automatic framework for high-throughput materials discovery

Accelerating high-throughput searches for new alloys with active learning of interatomic potentials

The high-throughput highway to computational materials design

Examination of computed aluminum grain boundary structures and energies that span the 5D space of crystallographic character

Efficiency of Generalized Regular k-point grids

Machine learning predictions of high-Curie-temperature materials

Hafnium binary alloys from experiments and first principles

The AFLOW Library of Crystallographic Prototypes: Part 3

Tensor-Reduced Atomic Density Representations

Machine-learned interatomic potentials for alloys and alloy phase diagrams

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

Accelerating training of MLIPs through small-cell training

Substitution with vision

A Set of Moment Tensor Potentials for Zirconium with Increasing Complexity

Cluster expansion made easy with Bayesian compressive sensing

Comprehensive Search for New Phases and Compounds in Binary Alloy Systems Based on Platinum-Group Metals, Using a Computational First-Principles Approach

Uncovering Compounds by Synergy of Cluster Expansion and High-Throughput Methods

Machine-learned multi-system surrogate models for materials prediction

Generating derivative superstructures for systems with high configurational freedom

Discovering the building blocks of atomic systems using machine learning: application to grain boundaries

Generating derivative structures at a fixed concentration

Evolutionary approach for determining first-principles hamiltonians

Generalized regular k-point grid generation on the fly

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