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Nanoscale|March 12, 2024
Accelerating the prediction of inorganic surfaces with machine learning interatomic potentialsKyle Noordhoek, Christopher J BartelNano Letters|December 27, 2024
Unraveling the Growth Dynamics of Rutile Sn<sub>1-</sub>Ge<sub></sub>O<sub>2</sub> Using Theory and ExperimentFengdeng Liu, Nathan J Szymanski, Kyle Noordhoek, et al.Patterns (New York, N.Y.)|November 25, 2021
Data-centric approach to improve machine learning models for inorganic materialsChristopher J BartelMaterials Horizons|July 11, 2025
Establishing baselines for generative discovery of inorganic crystalsNathan J Szymanski, Christopher J BartelChemical Communications (Cambridge, England)|January 22, 2020
Computational investigation of chalcogenide spinel conductors for all-solid-state Mg batteriesJulius Koettgen, Christopher J Bartel, Gerbrand CederMaterials Horizons|July 3, 2026
Thermodynamic assessment of machine learning models for solid-state synthesis predictionJane Schlesinger, Simon Hjaltason, Nathan J Szymanski, et al.Nature Communications|November 1, 2023
Autonomous and dynamic precursor selection for solid-state materials synthesisNathan 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 HoppingChristopher 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 CaJulius 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 literatureTanjin He, Haoyan Huo, Christopher J Bartel, et al.Pageof 4