University of Cambridge > Talks.cam > Machine learning in Physics, Chemistry and Materials discussion group (MLDG) > Data-Efficient Machine Learning with Chemical and Physical Priors

Data-Efficient Machine Learning with Chemical and Physical Priors

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

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

While machine learning is often discussed in a ‘big data’ context, for many chemical applications the generation of large reference databases can be prohibitive. I will talk about how the data-efficiency of machine learning models can be improved by using chemical or physical priors. In particular, I will discuss the role of size-extensivity, physical baseline models and hybrid approaches that integrate electronic structure theory and machine learning.

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.

 

© 2006-2021 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity