University of Cambridge > Talks.cam > Machine learning in Physics, Chemistry and Materials discussion group (MLDG) > Combining Quantum Mechanical Response Operators and Machine Learning.

Combining Quantum Mechanical Response Operators and Machine Learning.

Add to your list(s) Download to your calendar using vCal

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.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2020 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity