Combining Quantum Mechanical Response Operators and Machine Learning.

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• Felix Faber
• Monday 17 February 2020, 17:00-17:30
• Mott Seminar (531) room, top floor of the Mott Building, in the Cavendish Laboratory, West Cambridge..

If you have a question about this talk, please contact Bingqing Cheng .

The role of response operators is well established in quantum mechanics. Many relevant observables can be understood as response properties, obtained through the use of response operators and perturbation theory. Forces and dipole moments are two relatively simple examples of response properties and correspond to the change in energy when respectively perturbing the nuclear positions or an external electric field. I will discuss how to leverage such response operators to improve machine learning predictions of quantum mechanical properties of compounds. I will also draw a connection between the electron density and energy via response operators, and propose a way to incorporate this into a machine learning model for predicting both energies and electron densities.

This talk is part of the Machine learning in Physics, Chemistry and Materials discussion group (MLDG) series.

This talk is included in these lists:

• Hanchen DaDaDash
• Lennard-Jones Centre external
• Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
• Mott Seminar (531) room, top floor of the Mott Building, in the Cavendish Laboratory, West Cambridge.

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