Physics Colloquium: Machine Learning approach to Quantum Embedding

Physics Colloquium
Machine Learning approach to Quantum Embedding simulations

Dr. Nicola Lanatà
Assistant Professor
Aarhus University

Register here for Zoom Link
This seminar may be attended in person in 2140 Slaughter Hall or online via Zoom.

Abstract:
A cardinal obstacle to performing quantum-mechanical simulations of strongly correlated matter is that, with the theoretical tools presently available, sufficiently accurate computations are often too expensive to be ever feasible. Here we design a computational framework combining quantum-embedding (QE) methods with machine learning. This allows us to bypass altogether the most computationally expensive components of QE algorithms, making their overall cost comparable to classic approximations to density functional theory. We perform benchmark calculations of a series of models and real materials, showing that our method accurately describes the electronic structure, reducing by orders of magnitude the computational cost. We argue that, by producing a larger-scale set of training data, it will become possible to apply our machine-learning approach to systems with arbitrary stoichiometries and crystal structures, paving the way to virtually infinite applications in condensed matter physics, chemistry, and materials science.

Speaker Bio:
Nicola Lanatà obtained his PhD in “Theory and Numerical Simulation in Condensed Matter Systems” at the International School for Advanced Studies (SISSA-ISAS) (Italy) in October 2009, under the supervision of Prof. Michele Fabrizio. He subsequently worked for two years as a postdoc at the Gothenburg University (Sweden), and for another three years at Rutgers University (USA), under the supervision of Prof. Gabriel Kotliar. In 2015, he joined the National High Magnetic Field Laboratory (MagLab) as a Dirac Fellow. Since 2018 he is Assistant Professor at Aarhus University (Denmark) and Nordic Assistant Professor at NORDITA (Sweden). His research activity mainly concerns the study of strongly correlated quantum matter and the development of new theoretical and computational methods.

Intended Audience:
Beginners, undergraduates, graduates, experts. Those with interest in the topic.

To request an interpreter, please visit myaccess.rit.edu