University of Cambridge > Talks.cam > Lennard-Jones Centre > Random sampling versus active learning algorithms for machine learning potentials of quantum liquid water

  Browse A-Z

Log in

Googledetails Microsoftdetails

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

Random sampling versus active learning algorithms for machine learning potentials of quantum liquid water

Download to your calendar using vCal

If you have a question about this talk, please contact Eszter Varga-Umbrich .

Today, active learning is routinely used in training of machine learning potentials, but the efficacy of active learning across different systems is not well-tested. We have employed active learning algorithms based on committee disagreement for the training of a high-dimensional neural network potential for quantum liquid water at ambient conditions. Notably, we compare active to random data selection, given the same pool of candidate structures. I will discuss in detail the performance of active learning, at the level of computational requirements, train and test errors, and the quality of the final potentials in simulations including nuclear quantum effects.

This talk is part of the Lennard-Jones Centre series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Queens College, Cambridge Dept of Engineering Room B16, Faculty of Law

Other talks

Afternoon Break Sera Markoff Inaugural Lecture for the Plumian Professorship in Astronomy and Experimental Philosophy - 'Staring into the heart of darkness: from theory to the direct imaging of black holes' Group Work A Novel Diffusion Model based Approach for Sleep Music Generation Title to be confirmed Dr Dinis Calado, Senior Group Leader, Immunity and Cancer Laboratory, the Francis Crick Institute
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity