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Machine learning for interatomic potential models

The Journal of chemical physics, 2020-02, Vol.152 (5), p.050902-050902

2020

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Evaluation of Tavorite-Structured Cathode Materials for Lithium-Ion Batteries Using High-Throughput Computing

Chemistry of materials, 2011-09, Vol.23 (17), p.3854-3862

2011

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High-performance transition metal-doped Pt3Ni octahedra for oxygen reduction reaction

Science (American Association for the Advancement of Science), 2015-06, Vol.348 (6240), p.1230-1234

2015

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Ensemble Effect in Bimetallic Electrocatalysts for CO2 Reduction

Journal of the American Chemical Society, 2019-10, Vol.141 (42), p.16635-16642

2019

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Identifying models of dielectric breakdown strength from high-throughput data via genetic programming

Scientific reports, 2017-12, Vol.7 (1), p.17594-12, Article 17594

2017

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Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory

Chemistry of materials, 2010-06, Vol.22 (12), p.3762-3767

2010

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Roles of Mo Surface Dopants in Enhancing the ORR Performance of Octahedral PtNi Nanoparticles

Nano letters, 2018-02, Vol.18 (2), p.798-804

2018

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Effect of Particle Size on Hydrogen Release from Sodium Alanate Nanoparticles

ACS nano, 2010-10, Vol.4 (10), p.5647-5656

2010

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Efficient generation of generalized Monkhorst-Pack grids through the use of informatics

Physical review. B, 2016-04, Vol.93 (15), Article 155109

2016

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Ordered Intermetallic Pd3Bi Prepared by an Electrochemically Induced Phase Transformation for Oxygen Reduction Electrocatalysis

ACS nano, 2019-09, Vol.13 (9), p.10818-10825

2019

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A high-throughput infrastructure for density functional theory calculations

Computational materials science, 2011-06, Vol.50 (8), p.2295-2310

2011

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Mechanistic Insights for Low-Overpotential Electroreduction of CO2 to CO on Copper Nanowires

ACS catalysis, 2017-12, Vol.7 (12), p.8578-8587

2017

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Low-Overpotential Electroreduction of Carbon Monoxide Using Copper Nanowires

ACS catalysis, 2017-07, Vol.7 (7), p.4467-4472

2017

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A Density Functional Theory Study of Hydrogen Adsorption in MOF-5

The journal of physical chemistry. B, 2005-09, Vol.109 (38), p.17974-17983

2005

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Improved Prediction of Nanoalloy Structures by the Explicit Inclusion of Adsorbates in Cluster Expansions

Journal of physical chemistry. C, 2018-08, Vol.122 (31), p.18040-18047

2018

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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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Origins of hole traps in hydrogenated nanocrystalline and amorphous silicon revealed through machine learning

Physical review. B, Condensed matter and materials physics, 2014-03, Vol.89 (11), Article 115202

2014

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Differential Surface Elemental Distribution Leads to Significantly Enhanced Stability of PtNi-Based ORR Catalysts

Matter, 2019-12, Vol.1 (6), p.1567-1580

2019

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Fast, accurate, and transferable many-body interatomic potentials by symbolic regression

npj computational materials, 2019-11, Vol.5 (1), p.1-11, Article 112

2019

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A database of low-energy atomically precise nanoclusters

Scientific data, 2023-05, Vol.10 (1), p.308-308, Article 308

2023

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Catalytic Activity Maps for Alloy Nanoparticles

Journal of the American Chemical Society, 2023-04, Vol.145 (13), p.7352-7360

2023

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Theoretical Insights into the Effects of Oxidation and Mo-Doping on the Structure and Stability of Pt–Ni Nanoparticles

Nano letters, 2016-12, Vol.16 (12), p.7748-7754

2016

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Accelerated prediction of atomically precise cluster structures using on-the-fly machine learning

npj computational materials, 2022-08, Vol.8 (1), p.1-10, Article 173

2022

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Rational Design of Pt3Ni Surface Structures for the Oxygen Reduction Reaction

Journal of physical chemistry. C, 2015-08

2015

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The Rockefeller Foundation, the Social Sciences, and the Humanities in the Cold War

Journal of cold war studies, 2013-07, Vol.15 (3), p.108-108

Mueller, Tim B

2013

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Machine learning for interatomic potential models

Evaluation of Tavorite-Structured Cathode Materials for Lithium-Ion Batteries Using High-Throughput Computing

High-performance transition metal-doped Pt3Ni octahedra for oxygen reduction reaction

Ensemble Effect in Bimetallic Electrocatalysts for CO2 Reduction

Identifying models of dielectric breakdown strength from high-throughput data via genetic programming

Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory

Roles of Mo Surface Dopants in Enhancing the ORR Performance of Octahedral PtNi Nanoparticles

Effect of Particle Size on Hydrogen Release from Sodium Alanate Nanoparticles

Efficient generation of generalized Monkhorst-Pack grids through the use of informatics

Ordered Intermetallic Pd3Bi Prepared by an Electrochemically Induced Phase Transformation for Oxygen Reduction Electrocatalysis

A high-throughput infrastructure for density functional theory calculations

Mechanistic Insights for Low-Overpotential Electroreduction of CO2 to CO on Copper Nanowires

Low-Overpotential Electroreduction of Carbon Monoxide Using Copper Nanowires

A Density Functional Theory Study of Hydrogen Adsorption in MOF-5

Improved Prediction of Nanoalloy Structures by the Explicit Inclusion of Adsorbates in Cluster Expansions

Machine-learned multi-system surrogate models for materials prediction

Origins of hole traps in hydrogenated nanocrystalline and amorphous silicon revealed through machine learning

Differential Surface Elemental Distribution Leads to Significantly Enhanced Stability of PtNi-Based ORR Catalysts

Fast, accurate, and transferable many-body interatomic potentials by symbolic regression

A database of low-energy atomically precise nanoclusters

Catalytic Activity Maps for Alloy Nanoparticles

Theoretical Insights into the Effects of Oxidation and Mo-Doping on the Structure and Stability of Pt–Ni Nanoparticles

Accelerated prediction of atomically precise cluster structures using on-the-fly machine learning

Rational Design of Pt3Ni Surface Structures for the Oxygen Reduction Reaction

The Rockefeller Foundation, the Social Sciences, and the Humanities in the Cold War

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