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Five High-Impact Research Areas in Machine Learning for Materials Science

Chemistry of materials, 2019-12, Vol.31 (23), p.9579-9581

Meredig, Bryce

2019

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Industrial materials informatics: Analyzing large-scale data to solve applied problems in R&D, manufacturing, and supply chain

Current opinion in solid state & materials science, 2017-06, Vol.21 (3), p.159-166

Meredig, Bryce

2017

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A hybrid computational–experimental approach for automated crystal structure solution

Nature materials, 2013-02, Vol.12 (2), p.123-127

2013

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High-Throughput Computational Screening of New Li-Ion Battery Anode Materials

Advanced energy materials, 2013-02, Vol.3 (2), p.252-262

2013

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High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds

Chemistry of materials, 2016-10, Vol.28 (20), p.7324-7331

2016

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Materials Data Infrastructure: A Case Study of the Citrination Platform to Examine Data Import, Storage, and Access

JOM (1989), 2016-08, Vol.68 (8), p.2031-2034

2016

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Dissolving the Periodic Table in Cubic Zirconia: Data Mining to Discover Chemical Trends

Chemistry of materials, 2014-03, Vol.26 (6), p.1985-1991

2014

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Data mining our way to the next generation of thermoelectrics

Scripta materialia, 2016-01, Vol.111, p.10-15

2016

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Expanded dataset of mechanical properties and observed phases of multi-principal element alloys

Scientific data, 2020-12, Vol.7 (1), p.430-430, Article 430

2020

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The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies

npj computational materials, 2015-12, Vol.1 (1), p.15010-15010, Article 15010

2015

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Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties

APL materials, 2016-05, Vol.4 (5), p.053213-053213-11

2016

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Machine-learned metrics for predicting the likelihood of success in materials discovery

npj computational materials, 2020-08, Vol.6 (1), Article 131

2020

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Hackathon aims to solve materials problems

MRS bulletin, 2015-04, Vol.40 (4), p.366-370

2015

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Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery

Molecular systems design & engineering, 2018-10, Vol.3 (5), p.819-825

2018

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Machine Learning–Based Reduce Order Crystal Plasticity Modeling for ICME Applications

Integrating materials and manufacturing innovation, 2018-12, Vol.7 (4), p.214-230

2018

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Utilization of machine learning to accelerate colloidal synthesis and discovery

The Journal of chemical physics, 2021-06, Vol.154 (22), p.224201-224201

2021

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Ordering of Poly(3-hexylthiophene) Nanocrystallites on the Basis of Substrate Surface Energy

ACS nano, 2009-10, Vol.3 (10), p.2881-2886

2009

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Single-Crystal Automated Refinement (SCAR): A Data-Driven Method for Determining Inorganic Structures

Inorganic chemistry, 2019-07, Vol.58 (14), p.9004-9015

2019

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AutoMat: Automated materials discovery for electrochemical systems

MRS bulletin, 2022-10, Vol.47 (10), p.1036-1044

2022

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Data‐driven glass/ceramic science research: Insights from the glass and ceramic and data science/informatics communities

Journal of the American Ceramic Society, 2019-11, Vol.102 (11), p.6385-6406

2019

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Beyond bulk single crystals: A data format for all materials structure–property–processing relationships

MRS bulletin, 2016-08, Vol.41 (8), p.617-623

2016

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Materials science with large-scale data and informatics: Unlocking new opportunities

MRS bulletin, 2016-05, Vol.41 (5), p.399-409

2016

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Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)

JOM (1989), 2013-11, Vol.65 (11), p.1501-1509

2013

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High-Dimensional Materials and Process Optimization Using Data-Driven Experimental Design with Well-Calibrated Uncertainty Estimates

Integrating materials and manufacturing innovation, 2017-09, Vol.6 (3), p.207-217

2017

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Combinatorial screening for new materials in unconstrained composition space with machine learning

Physical review. B, Condensed matter and materials physics, 2014-03, Vol.89 (9), Article 094104

2014

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Five High-Impact Research Areas in Machine Learning for Materials Science

Industrial materials informatics: Analyzing large-scale data to solve applied problems in R&D, manufacturing, and supply chain

A hybrid computational–experimental approach for automated crystal structure solution

High-Throughput Computational Screening of New Li-Ion Battery Anode Materials

High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds

Materials Data Infrastructure: A Case Study of the Citrination Platform to Examine Data Import, Storage, and Access

Dissolving the Periodic Table in Cubic Zirconia: Data Mining to Discover Chemical Trends

Data mining our way to the next generation of thermoelectrics

Expanded dataset of mechanical properties and observed phases of multi-principal element alloys

The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies

Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties

Machine-learned metrics for predicting the likelihood of success in materials discovery

Hackathon aims to solve materials problems

Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery

Machine Learning–Based Reduce Order Crystal Plasticity Modeling for ICME Applications

Utilization of machine learning to accelerate colloidal synthesis and discovery

Ordering of Poly(3-hexylthiophene) Nanocrystallites on the Basis of Substrate Surface Energy

Single-Crystal Automated Refinement (SCAR): A Data-Driven Method for Determining Inorganic Structures

AutoMat: Automated materials discovery for electrochemical systems

Data‐driven glass/ceramic science research: Insights from the glass and ceramic and data science/informatics communities

Beyond bulk single crystals: A data format for all materials structure–property–processing relationships

Materials science with large-scale data and informatics: Unlocking new opportunities

Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)

High-Dimensional Materials and Process Optimization Using Data-Driven Experimental Design with Well-Calibrated Uncertainty Estimates

Combinatorial screening for new materials in unconstrained composition space with machine learning

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