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Volume 5 Issue 9, September 2023

Crystal Hamiltonian graph neural networks

The need to quickly discover new materials and to understand their underlying physics in the presence of complex electron interactions calls for advanced simulation tools. Deng et al. propose CHGNet, a graph-neural-network-based machine learning interatomic potential that incorporates charge information. Pretrained on over 1.5 million inorganic crystal structures, CHGNet opens new opportunities for insights into ionic systems with charge interactions.

See Deng et al.

Image: Bowen Deng, University of California, Berkeley. Cover design: Thomas Phillips


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