Publications by authors named "Honggeng Tao"

Article Synopsis
  • Deep-learning density functional theory (DFT) has the potential to speed up material discovery and change how materials research is conducted.
  • Current approaches in this area mainly depend on supervised learning, which keeps neural networks and DFT developments separate.
  • This research introduces a new theoretical framework that integrates neural network optimization with DFT computations, allowing for more efficient, physics-informed unsupervised learning, and demonstrates its effectiveness through new algorithms in a differential DFT code.
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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.

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Nonreciprocity is important in both optical information processing and topological photonics studies. Conventional principles for realizing nonreciprocity rely on magnetic fields, spatiotemporal modulation, or nonlinearity. Here we propose a generic principle for generating nonreciprocity by taking advantage of energy loss, which is usually regarded as harmful.

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