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Relative grain boundary energies from triple junction geometry: Limitations to assuming the Herring condition in nanocrystalline thin films

Acta materialia, 2023-01, Vol.242, p.118476, Article 118476

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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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 multi-system surrogate models for materials prediction

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

2019

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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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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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Ordered Structures in Rhenium Binary Alloys from First-Principles Calculations

Journal of the American Chemical Society, 2011-01, Vol.133 (1), p.158-163

2011

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The New Face of Rhodium Alloys: Revealing Ordered Structures from First Principles

Journal of the American Chemical Society, 2010-01, Vol.132 (2), p.833-837

2010

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Machine learning for alloys

Nature reviews. Materials, 2021-08, Vol.6 (8), p.730-755

2021

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First principles thermodynamical modeling of the binodal and spinodal curves in lead chalcogenides

Physical chemistry chemical physics : PCCP, 2016-02, Vol.18 (6), p.5005-5011

2016

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Robustness of the cluster expansion: Assessing the roles of relaxation and numerical error

Physical review. B, 2017-07, Vol.96 (1), Article 014107

2017

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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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AFLOW: An automatic framework for high-throughput materials discovery

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

2012

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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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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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Hafnium binary alloys from experiments and first principles

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

2010

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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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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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A computational high-throughput search for new ternary superalloys

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

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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Finding new phases for precipitate-hardening in platinum and palladium alloys

Computational materials science, 2012-01, Vol.51 (1), p.331-339

2012

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Machine-Learning Informed Representations for Grain Boundary Structures

Frontiers in materials, 2019-07, Vol.6

2019

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Using genetic algorithms to map first-principles results to model hamiltonians : Application to the generalized ising model for alloys

Physical review. B, Condensed matter and materials physics, 2005-10, Vol.72 (16), p.165113.1-165113.13, Article 165113

2005

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Relative grain boundary energies from triple junction geometry: Limitations to assuming the Herring condition in nanocrystalline thin films

Machine-learned interatomic potentials for alloys and alloy phase diagrams

Tensor-Reduced Atomic Density Representations

Machine-learned multi-system surrogate models for materials prediction

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

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

Ordered Structures in Rhenium Binary Alloys from First-Principles Calculations

The New Face of Rhodium Alloys: Revealing Ordered Structures from First Principles

Machine learning for alloys

First principles thermodynamical modeling of the binodal and spinodal curves in lead chalcogenides

Robustness of the cluster expansion: Assessing the roles of relaxation and numerical error

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

AFLOW: An automatic framework for high-throughput materials discovery

Machine learning predictions of high-Curie-temperature materials

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

Hafnium binary alloys from experiments and first principles

Accelerating training of MLIPs through small-cell training

A Set of Moment Tensor Potentials for Zirconium with Increasing Complexity

Cluster expansion made easy with Bayesian compressive sensing

A computational high-throughput search for new ternary superalloys

Generating derivative structures at a fixed concentration

Finding new phases for precipitate-hardening in platinum and palladium alloys

Machine-Learning Informed Representations for Grain Boundary Structures

Using genetic algorithms to map first-principles results to model hamiltonians : Application to the generalized ising model for alloys

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