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ORCIDChristopher J. Bartel
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2020 – today
- 2025
[i5]Nathan J. Szymanski, Christopher J. Bartel:
Establishing baselines for generative discovery of inorganic crystals. CoRR abs/2501.02144 (2025)- 2023
- [j2]Bowen Deng, Peichen Zhong
, KyuJung Jun, Janosh Riebesell, Kevin Han, Christopher J. Bartel, Gerbrand Ceder:
CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling. Nat. Mac. Intell. 5(9): 1031-1041 (2023) - [i4]Tanjin He, Haoyan Huo, Christopher J. Bartel, Zheren Wang, Kevin Cruse, Gerbrand Ceder:
Inorganic synthesis recommendation by machine learning materials similarity from scientific literature. CoRR abs/2302.02303 (2023) - [i3]Bowen Deng, Peichen Zhong, KyuJung Jun, Kevin Han, Christopher J. Bartel, Gerbrand Ceder:
CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling. CoRR abs/2302.14231 (2023) - [i2]Kyle Noordhoek, Christopher J. Bartel:
Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials. CoRR abs/2312.11708 (2023) - 2021
- [j1]Christopher J. Bartel:
Data-centric approach to improve machine learning models for inorganic materials. Patterns 2(11): 100382 (2021) - [i1]Nathan J. Szymanski, Christopher J. Bartel, Yan Zeng, Qingsong Tu, Gerbrand Ceder:
A probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra. CoRR abs/2103.16664 (2021)
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