## Description

Back to topMachine learning (ML) approaches are transforming the field of electronic structure calculations. This is particularly true for Density Functional Theory (DFT), the most widely used quantum mechanical approach in computational materials science. It allows to recast the search for the ground state of the Schrödinger operator into the minimization of a functional of the electronic density of the system, a function in three variables only. The caveat is that the form of the functional is unknown. The very efficient Kohn-Sham (KS) scheme proposed in the 60’s, in which only the exchange-correlation (XC) energy needs to be approximated, still faces some limitations, despite intense efforts in the physics and chemistry communities. Machine learning (ML) is promising for improving density-functional approximations, either to find the best combination of current XC approximations, or to construct new XC functionals or even to produce pure density functionals to bypass the need to solve KS equations. Besides tackling the total energy in that way, ML can also be used to predict the electronic structure from higher-level methodology like Green-function approaches. Moreover, ML is also very promising for other complementary electronic structure methods, e.g. to solve the bottleneck of the parametrization of tight-binding Hamiltonians on DFT calculations or to improve the efficiency of the highly-accurate Quantum Monte Carlo methods that have prohibitive computational cost for large systems.

## Organizers

Back to top## Speakers

Back to top## Schedule

Back to top**Speaker: **Attila Cangi (Helmholtz-Zentrum Dresden-Rossendorf (HZDR))

**Speaker: **Berend Smit (EPFL (Ecole Polytechnique Fédérale de Lausanne))

**Speaker: **Michael Lindsey (University of California, Berkeley (UC Berkeley))

**Speaker: **Roberto Car (Princeton University)

**Speaker: **David Mazziotti (University of Chicago)

**Speaker: **Marivi Fernández-Serra (SUNY Stony Brook University)

**Speaker: **Kieron Burke (University of California, Irvine (UCI))

**Speaker: **Boris Kozinsky (Harvard University)

**Speaker: **Laura Gagliardi (University of Chicago)

**Speaker: **Yuehaw Khoo (University of Chicago)

**Speaker: **Giuseppe Carleo (EPFL (Ecole Polytechnique Fédérale de Lausanne))

**Speaker: **Jonathan Weare (Courant Institute of Mathematical Sciences)

**Speaker: **Frank Noé (Freie Universität Berlin)

**Speaker: **Nilin Abrahamsen (University of California, Berkeley (UC Berkeley))

**Speaker: **Jianfeng Lu (Duke University)

**Speaker: **Heather Kulik (Massachusetts Institute of Technology (MIT))

**Speaker: **Santiago Rigamonti (Humboldt-Universität zu Berlin)

**Speaker: **Lucia Reining (Centre National de la Recherche Scientifique (CNRS))

**Speaker: **Fabien Bruneval (CEA – Saclay)

**Speaker: **Maria Chan (Argonne National Laboratory)

**Speaker: **Luca Ghiringhelli (Friedrich-Alexander-Universität (FAU) Erlangen-Nurenberg)

**Speaker: **Alexandre Tkatchenko (University of Luxembourg)

**Speaker: **Tess Smidt (Massachusetts Institute of Technology (MIT))

## Videos

Scalable Machine Learning for Predicting the Electronic Structure of Matter

Attila Cangi

March 25, 2024

Reducing the Quantum Many-Electron Problem to Two Electrons with Machine Learning

David Mazziotti

March 25, 2024

Physical constraints in machine learning models of electronic and atomic interactions

Boris Kozinsky

March 26, 2024

Deep Variational Quantum Monte Carlo – a way forward for strongly correlated systems?

Frank Noé

March 27, 2024

A Kaczmarz-inspired approach to accelerate the optimization of neural network wavefunctions

Nilin Abrahamsen

March 27, 2024

Expressing The Density Matrix as Functional Of The Density: How To Profit From Machine Learning?

Lucia Reining

March 28, 2024

Extrapolating Unconverged GW Energies up to the Complete Basis Set Limit with a Linear Model

Fabien Bruneval

March 28, 2024

Bridging Scales in Materials Modeling With Occam-Shaved Machine Learning

Luca Ghiringhelli

March 29, 2024

Exploring Compositional and Configurational Chemical Space with Explainable AI

Alexandre Tkatchenko

March 29, 2024