Nature Machine Intelligence: Crystal Hamiltonian graph neural networks
The image designed by Sphere Studio is selected as the Cover of Nature Machine Intelligence in September.
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.