Universal materials model of deep-learning density functional theory Hamiltonian

Abstract

Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence, but how to achieve this fantastic and challenging objective remains elusive. Here, we propose a feasible pathway to address this paramount pursuit by developing universal materials models of deep-learning density functional theory Hamiltonian (DeepH), enabling computational modeling of the complicated structure-property relationship of materials in general. By constructing a large materials database and substantially improving the DeepH method, we obtain a universal materials model of DeepH capable of handling diverse elemental compositions and material structures, achieving remarkable accuracy in predicting material properties. We further showcase a promising application of fine-tuning universal materials models for enhancing specific materials models. This work not only demonstrates the concept of DeepH's universal materials model but also lays the groundwork for developing large materials models, opening up significant opportunities for advancing artificial intelligence-driven materials discovery.

References in corpus (12)

• Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
• Nearsightedness of Electronic Matter
• AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance
• Deep-Learning Density Functional Theory Hamiltonian for Efficient ab initio Electronic-Structure Calculation
• Workflows in AiiDA: Engineering a high-throughput, event-based engine for robust and modular computational workflows
• General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian
• Transferable E(3) equivariant parameterization for Hamiltonian of molecules and solids
• Deep-learning electronic-structure calculation of magnetic superstructures
• Deep-learning density functional perturbation theory
• Neural network representation of electronic structure from $ab$ $initio$ molecular dynamics
• SE(3)-equivariant prediction of molecular wavefunctions and electronic densities
• A Self-Adaptive First-Principles Approach for Magnetic Excited States

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