A probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra. - Nathan J. Szymanski; Christopher J. Bartel; Yan Zeng; Qingsong Tu; Gerbrand Ceder (CoRR, 2021)

Data-centric approach to improve machine learning models for inorganic materials. - Christopher J. Bartel (Patterns, 2021)

Inorganic synthesis recommendation by machine learning materials similarity from scientific literature. - Tanjin He; Haoyan Huo; Christopher J. Bartel; Zheren Wang; Kevin Cruse; Gerbrand Ceder (CoRR, 2023)

CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling. - Bowen Deng; Peichen Zhong; KyuJung Jun; Kevin Han; Christopher J. Bartel; Gerbrand Ceder (CoRR, 2023)

Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials. - Kyle Noordhoek; Christopher J. Bartel (CoRR, 2023)

CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling. - Bowen Deng; Peichen Zhong; KyuJung Jun; Janosh Riebesell; Kevin Han; Christopher J. Bartel; Gerbrand Ceder (Nat. Mac. Intell., 2023)

Establishing baselines for generative discovery of inorganic crystals. - Nathan J. Szymanski; Christopher J. Bartel (CoRR, 2025)