Date/Time
Date(s) - 07/03/2022
1:00 pm - 2:00 pm

We are very pleased to welcome Dr. Rodrigo Vargas-Hernandez, Postdoctoral Researcher at the University of Toronto, on March 7.

We will be offering the seminar in a hybrid format (details below).

Hope to see you there!

Presenter: Dr. Rodrigo Vargas-Hernandez

Title: Inverse design with machine learning

Date: Monday, March 7,2022

Time: 1:00-2:00

Room: ABB-163

Zoom: email chemgrad@mcmaster.ca for the link

Abstract: Part of the success of machine learning (ML) algorithms is the development of efficient and robust search algorithms. Searching algorithms can be classified into sampling and gradients-based methodologies. During this presentation, I will describe the possibility of constructing accurate physical models through inverse design protocols using machine learning search algorithms.

First, I will illustrate the use of Bayesian optimization (BO), an efficient probabilistic sampling optimizer, to select the optimal density functional model for a chemical system of interest. In the second part of the talk, I will present the application of differentiable programming (DP), a novel numerical tool for computing gradients, combined with gradient-based methods to optimize physical systems. Using these tools, we increase the energy transport of quantum systems and optimize any classical parameters needed in variational quantum eigensolvers.