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Efficient calculation of carrier scattering rates from first principles

Nature communications, 2021-04, Vol.12 (1), p.2222-2222, Article 2222

2021

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A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses

Nature communications, 2019-12, Vol.10 (1), p.5537-11, Article 5537

2019

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Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm

npj computational materials, 2020-09, Vol.6 (1), Article 138

2020

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When band convergence is not beneficial for thermoelectrics

Nature communications, 2021-06, Vol.12 (1), p.3425-8, Article 3425

2021

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New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships

Journal of materials research, 2016-04, Vol.31 (8), p.977-994

2016

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Recent advances and applications of deep learning methods in materials science

npj computational materials, 2022-04, Vol.8 (1), p.1-26, Article 59

2022

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A framework for quantifying uncertainty in DFT energy corrections

Scientific reports, 2021-07, Vol.11 (1), p.15496-15496, Article 15496

2021

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A critical examination of compound stability predictions from machine-learned formation energies

npj computational materials, 2020-07, Vol.6 (1), Article 97

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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New horizons in thermoelectric materials: Correlated electrons, organic transport, machine learning, and more

Journal of applied physics, 2019-05, Vol.125 (18)

2019

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A simple model for the entropy of melting of monatomic liquids

Applied physics letters, 2021-02, Vol.118 (8)

2021

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Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity

RSC advances, 2020-02, Vol.10 (10), p.6063-6081

2020

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Spinel compounds as multivalent battery cathodes: a systematic evaluation based on ab initio calculations

Energy & environmental science, 2015-01, Vol.8 (3), p.964-974

2015

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Data Mined Ionic Substitutions for the Discovery of New Compounds

Inorganic chemistry, 2011-01, Vol.50 (2), p.656-663

2011

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Accuracy of density functional theory in predicting formation energies of ternary oxides from binary oxides and its implication on phase stability

Physical review. B, Condensed matter and materials physics, 2012-04, Vol.85 (15), Article 155208

2012

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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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Matminer: An open source toolkit for materials data mining

Computational materials science, 2018-09, Vol.152 (C), p.60-69

2018

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Voltage, stability and diffusion barrier differences between sodium-ion and lithium-ion intercalation materials

Energy & environmental science, 2011-01, Vol.4 (9), p.3680-3688

2011

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A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds

Scientific reports, 2016-10, Vol.6 (1), p.34256-34256, Article 34256

2016

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Research Update: The materials genome initiative: Data sharing and the impact of collaborative ab initio databases

APL materials, 2016-05, Vol.4 (5), p.053102-053102-14

2016

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Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis

Computational materials science, 2013-02, Vol.68, p.314-319

2013

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Phosphates as Lithium-Ion Battery Cathodes: An Evaluation Based on High-Throughput ab Initio Calculations

Chemistry of materials, 2011-08, Vol.23 (15), p.3495-3508

2011

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Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows

Computational materials science, 2017-11, Vol.139 (C), p.140-152

2017

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Evaluation of sulfur spinel compounds for multivalent battery cathode applications

Energy & environmental science, 2016-01, Vol.9 (1), p.321-329

2016

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Assessing High-Throughput Descriptors for Prediction of Transparent Conductors

Chemistry of materials, 2018-11, Vol.30 (22), p.8375-8389

2018

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Efficient calculation of carrier scattering rates from first principles

A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses

Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm

When band convergence is not beneficial for thermoelectrics

New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships

Recent advances and applications of deep learning methods in materials science

A framework for quantifying uncertainty in DFT energy corrections

A critical examination of compound stability predictions from machine-learned formation energies

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

New horizons in thermoelectric materials: Correlated electrons, organic transport, machine learning, and more

A simple model for the entropy of melting of monatomic liquids

Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity

Spinel compounds as multivalent battery cathodes: a systematic evaluation based on ab initio calculations

Data Mined Ionic Substitutions for the Discovery of New Compounds

Accuracy of density functional theory in predicting formation energies of ternary oxides from binary oxides and its implication on phase stability

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

Matminer: An open source toolkit for materials data mining

Voltage, stability and diffusion barrier differences between sodium-ion and lithium-ion intercalation materials

A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds

Research Update: The materials genome initiative: Data sharing and the impact of collaborative ab initio databases

Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis

Phosphates as Lithium-Ion Battery Cathodes: An Evaluation Based on High-Throughput ab Initio Calculations

Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows

Evaluation of sulfur spinel compounds for multivalent battery cathode applications

Assessing High-Throughput Descriptors for Prediction of Transparent Conductors

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