Christopher J. Bartel

According to our database, Christopher J. Bartel authored at least 8 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Thermodynamic assessment of machine learning models for solid-state synthesis prediction.

Jane Schlesinger, Simon Hjaltason, Nathan J. Szymanski, Christopher J. Bartel

CoRR, February, 2026

2025

Establishing baselines for generative discovery of inorganic crystals.

Nathan J. Szymanski, Christopher J. Bartel

CoRR, January, 2025

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., September, 2023

Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials.

Kyle Noordhoek, Christopher J. Bartel

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

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

2021

Data-centric approach to improve machine learning models for inorganic materials.

Christopher J. Bartel

Patterns, 2021

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