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Patterns (New York, N.Y.)
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November 25, 2021
Data-centric approach to improve machine learning models for inorganic materials
Christopher J Bartel
Nanoscale
|
March 12, 2024
Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials
Kyle Noordhoek, Christopher J Bartel
Materials Horizons
|
July 11, 2025
Establishing baselines for generative discovery of inorganic crystals
Nathan J Szymanski, Christopher J Bartel
Chemical Communications (Cambridge, England)
|
January 22, 2020
Computational investigation of chalcogenide spinel conductors for all-solid-state Mg batteries
Julius Koettgen, Christopher J Bartel, Gerbrand Ceder
Materials Horizons
|
July 3, 2026
Thermodynamic assessment of machine learning models for solid-state synthesis prediction
Jane Schlesinger, Simon Hjaltason, Nathan J Szymanski, et al.
Nature Communications
|
November 1, 2023
Autonomous and dynamic precursor selection for solid-state materials synthesis
Nathan J Szymanski, Pragnay Nevatia, Christopher J Bartel, et al.
ACS Applied Materials & Interfaces
|
June 25, 2016
Aluminum Nitride Hydrolysis Enabled by Hydroxyl-Mediated Surface Proton Hopping
Christopher J Bartel, Christopher L Muhich, Alan W Weimer, et al.
Physical Chemistry Chemical Physics : PCCP
|
November 26, 2020
First-principles study of CaB<sub>12</sub>H<sub>12</sub> as a potential solid-state conductor for Ca
Julius Koettgen, Christopher J Bartel, Jimmy-Xuan Shen, et al.
Science Advances
|
June 9, 2023
Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature
Tanjin He, Haoyan Huo, Christopher J Bartel, et al.
Materials Horizons
|
November 30, 2021
Toward autonomous design and synthesis of novel inorganic materials
Nathan J Szymanski, Yan Zeng, Haoyan Huo, et al.
Page
of 4
Search research articles
Search
Showing results (1-10 of 32) with videos related to
Sort By:
Page
of 4
Patterns (New York, N.Y.)
|
November 25, 2021
Data-centric approach to improve machine learning models for inorganic materials
Christopher J Bartel
Nanoscale
|
March 12, 2024
Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials
Kyle Noordhoek, Christopher J Bartel
Materials Horizons
|
July 11, 2025
Establishing baselines for generative discovery of inorganic crystals
Nathan J Szymanski, Christopher J Bartel
Chemical Communications (Cambridge, England)
|
January 22, 2020
Computational investigation of chalcogenide spinel conductors for all-solid-state Mg batteries
Julius Koettgen, Christopher J Bartel, Gerbrand Ceder
Materials Horizons
|
July 3, 2026
Thermodynamic assessment of machine learning models for solid-state synthesis prediction
Jane Schlesinger, Simon Hjaltason, Nathan J Szymanski, et al.
Nature Communications
|
November 1, 2023
Autonomous and dynamic precursor selection for solid-state materials synthesis
Nathan J Szymanski, Pragnay Nevatia, Christopher J Bartel, et al.
ACS Applied Materials & Interfaces
|
June 25, 2016
Aluminum Nitride Hydrolysis Enabled by Hydroxyl-Mediated Surface Proton Hopping
Christopher J Bartel, Christopher L Muhich, Alan W Weimer, et al.
Physical Chemistry Chemical Physics : PCCP
|
November 26, 2020
First-principles study of CaB<sub>12</sub>H<sub>12</sub> as a potential solid-state conductor for Ca
Julius Koettgen, Christopher J Bartel, Jimmy-Xuan Shen, et al.
Science Advances
|
June 9, 2023
Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature
Tanjin He, Haoyan Huo, Christopher J Bartel, et al.
Materials Horizons
|
November 30, 2021
Toward autonomous design and synthesis of novel inorganic materials
Nathan J Szymanski, Yan Zeng, Haoyan Huo, et al.
Page
of 4