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Deep Learning many-body electronic structure

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If you have a question about this talk, please contact Bo Peng.

Deep learning with neural network wave functions is a powerful new approach for calculating the many-body electronic structure of molecules, materials, and physical models, addressing shared problems of strongly correlated electronic structure in fields of condensed matter physics and chemistry. In this talk, I will provide an overview of the many-body electronic structure problem and the development of related deep learning methods, and discuss our recent progresses, including a series of algorithmic developments, accurate ab initio solutions of molecular and solids, and exploring the fractional quantum Hall effect and correlated topological states of matter.

This talk is part of the Theory of Condensed Matter series.

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