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Machine learning as a solution to the electronic structure problem

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  • UserBeatriz G. del Rio (Georgia Tech)
  • ClockWednesday 26 May 2021, 16:00-17:00
  • HouseZoom.

If you have a question about this talk, please contact Chuck Witt.

An essential component of materials research is the use of simulations based on density functional theory (DFT), which imposes severe limitations on the size of the system under study. A promising development in recent years is the use of machine learning (ML) methodologies to train surrogate models with DFT data to predict quantum-accurate results for larger systems. Many successful ML models have been created to predict higher-level DFT results such as the total potential energy and atomic forces, and initial steps have been taken to create machine-learning based ML methodologies that can predict fundamental DFT outputs such as the charge density, wave functions and corresponding energy levels. In this talk, I will present our latest results using deep learning neural networks to learn and predict the electronic structure of a large variety of carbon allotropes, and its extension to hydrocarbons.

B. G. del Rio, C. Kuenneth, H. D. Tran, and R. Ramprasad, J. Phys. Chem. A 124 , 9496-9502 (2020).

This talk is part of the Electronic Structure Discussion Group series.

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