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A Framework for Quantifying Uncertainty in DFT Energy Corrections

Amanda Wang, Ryan Kingsbury, Matthew McDermott, Matthew Horton, Anubhav Jain, Shyue Ping Ong, Shyam Dwaraknath, Kristin Persson

Version 1 posted 19 May 2021

1,889

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Theoretical and Computational Chemistry

Working Paper

Is Domain Knowledge Necessary for Machine Learning Materials Properties?

Ryan Murdock, Steven Kauwe, Anthony Wang, Taylor Sparks

Version 1 posted 21 February 2020

1,095

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2

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Theoretical and Computational Chemistry

Working Paper

Can Machine Learning Find Extraordinary Materials?

Steven Kauwe, Jake Graser, Ryan Murdock, Taylor Sparks

Version 1 posted 09 August 2019

1,765

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Theoretical and Computational Chemistry

Working Paper

ChemML: A Machine Learning and Informatics Program Package for the Analysis, Mining, and Modeling of Chemical and Materials Data

Mojtaba Haghighatlari, Gaurav Vishwakarma, Doaa Altarawy, Ramachandran Subramanian, Bhargava Urala Kota, Aditya Sonpal, Srirangaraj Setlur, Johannes Hachmann

Version 1 posted 26 June 2019

1,885

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1

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Theoretical and Computational Chemistry

Working Paper

Mapping inorganic crystal chemical space

Hyunsoo Park, Anthony Onwuli, Keith T. Butler, Aron Walsh

Version 1 posted 02 January 2024

676

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Inorganic Chemistry

Working Paper

Graph-Text Contrastive Learning of Inorganic Crystal Structure toward a Foundation Model of Inorganic Materials

Keisuke Ozawa, Teppei Suzuki, Shunsuke Tonogai, Tomoya Itakura

Version 6 posted 15 April 2024

1,065

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Materials Science

Working Paper

Discovering Chemically Novel, High-Temperature Superconductors

Colton C. Seegmiller, Sterling G. Baird, Hasan M. Sayeed, Taylor D. Sparks

Version 4 posted 28 June 2023

1,078

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Inorganic Chemistry

Working Paper

The Earth Mover’s Distance as a Metric for the Space of Inorganic Compositions

Cameron Hargreaves, Matthew Dyer, Michael Gaultois, Vitaliy Kurlin, Matthew J Rosseinsky

Version 1 posted 10 August 2020

749

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Theoretical and Computational Chemistry

Working Paper

Molecular screening for solid–solid phase transition by machine learning

Daisuke Takagi, Kazuki Ishizaki, Toru Asahi, Takuya Taniguchi

Version 2 posted 19 October 2022

259

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1

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Theoretical and Computational Chemistry

Working Paper

Machine Learning for Materials Scientists: An Introductory Guide Towards Best Practices

Anthony Wang, Ryan Murdock, Steven Kauwe, Anton Oliynyk, Aleksander Gurlo, Jakoah Brgoch, Kristin Persson, Taylor Sparks

Version 1 posted 06 May 2020

2,664

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1

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34 results in materials informatics in Keywords: materials informatics

A Framework for Quantifying Uncertainty in DFT Energy Corrections

Is Domain Knowledge Necessary for Machine Learning Materials Properties?

Can Machine Learning Find Extraordinary Materials?

ChemML: A Machine Learning and Informatics Program Package for the Analysis, Mining, and Modeling of Chemical and Materials Data

Mapping inorganic crystal chemical space

Graph-Text Contrastive Learning of Inorganic Crystal Structure toward a Foundation Model of Inorganic Materials

Discovering Chemically Novel, High-Temperature Superconductors

The Earth Mover’s Distance as a Metric for the Space of Inorganic Compositions

Molecular screening for solid–solid phase transition by machine learning

Machine Learning for Materials Scientists: An Introductory Guide Towards Best Practices

34 results in Keywords: materials informatics