![]() Long-time and large-scale spin-polarized AIMD simulations critical for studying ion migrations, phase transformations and chemical reactions are challenging and extremely computing intensive 8, 9. The charge-density distribution and corresponding energy can be obtained by solving the Kohn–Sham equation 7. ![]() However, these empirical methods are often not accurate enough to capture complex electron interactions.Īb initio molecular dynamics (AIMD) with density functional theory (DFT) can produce high-fidelity results with quantum-mechanical accuracy by explicitly computing the electronic structure within the density functional approximation. ![]() Methodology developments in the field of polarizable force fields such as the electronegativity equalization method 4, chemical potential equalization 5 and charge equilibration 6 realize charge evolution via the redistribution of atomic partial charge. Classical force fields treat the charge as an atomic property that is assigned to every atom a priori 2, 3. Despite their importance, accurate modelling of electron interactions or their subtle effects in MD simulations remains a major challenge. Technological relevance of such simulations requires rigorous chemical specificity, which originates from the orbital occupancy of atoms. They enable the study of reactivity, degradation, interfacial reactions, transport in partially disordered structures and other heterogeneous phenomena relevant for the application of complex materials in technology. Large-scale simulations, such as molecular dynamics (MD), are essential tools in the computational exploration of solid-state materials 1. We highlight the significance of charge information for capturing appropriate chemistry and provide insights into ionic systems with additional electronic degrees of freedom that cannot be observed by previous MLIPs. We demonstrate several applications of CHGNet in solid-state materials, including charge-informed molecular dynamics in Li xMnO 2, the finite temperature phase diagram for Li xFePO 4 and Li diffusion in garnet conductors. The explicit inclusion of magnetic moments enables CHGNet to learn and accurately represent the orbital occupancy of electrons, enhancing its capability to describe both atomic and electronic degrees of freedom. CHGNet is pretrained on the energies, forces, stresses and magnetic moments from the Materials Project Trajectory Dataset, which consists of over 10 years of density functional theory calculations of more than 1.5 million inorganic structures. Here we present the Crystal Hamiltonian Graph Neural Network (CHGNet), a graph neural network-based machine-learning interatomic potential (MLIP) that models the universal potential energy surface. Although classical force fields often fail to describe the coupling between electronic states and ionic rearrangements, the more accurate ab initio molecular dynamics suffers from computational complexity that prevents long-time and large-scale simulations, which are essential to study technologically relevant phenomena. Large-scale simulations with complex electron interactions remain one of the greatest challenges for atomistic modelling.
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