Following the success of large language models, the concept of large materials models as deep-learning computational models for materials design has attracted great interest. Nevertheless, the task of acquiring large materials models appears to be quite challenging, given the inherent complexity of the structure-property relationship in materials.
A research team from Tsinghua University, led by Prof. Yong Xu and Prof. Wenhui Duan, sought to overcome this challenge by developing large materials models using the deep-learning density functional theory Hamiltonian (DeepH) method.
Density functional theory (DFT) has emerged as a highly valuable first-principles approach for computational materials design and is one of the most popular methods in computational materials science. The DFT Hamiltonian serves as a fundamental quantity in DFT computations, enabling the straightforward derivation of all other physical quantities, including total energy, charge density, band structure, physical responses, etc.
The team's work is published in the journal Science Bulletin.
While the DeepH method has been widely applied to study specific materials, developing a universal materials model of DeepH capable of handling diverse material structures across most elements of the periodic table remains elusive. DeepH leverages prior knowledge of physics to enhance its model performance.
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