This event is part of Data-Driven Materials Informatics View Details

Machine Learning in Electronic-Structure Theory

Description

Back to top

Machine 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
C D
Claudia Draxl Humboldt-Universität zu Berlin
G G
Giulia Galli University of Chicago
L L
Lin Lin University of California Berkeley
F W
François Willaime CEA Saclay

Speakers

Back to top
N A
Nilin Abrahamsen University of California, Berkeley (UC Berkeley)
F B
Fabien Bruneval CEA
K B
Kieron Burke University of California, Irvine (UCI)
R C
Roberto Car Princeton University
G C
Giuseppe Carleo EPFL
M C
Maria Chan Argonne National Laboratory
M F
Marivi Fernández-Serra Stony Brook University
L G
Laura Gagliardi University of Chicago
A G
Attila Ganci Helmholtz-Zentrum Dresden-Rossendorf (HZDR)
L G
Luca Ghiringhelli Humboldt-Universität zu Berlin
Y K
Yuehaw Khoo University of Chicago
B K
Boris Kozinsky Harvard University
H K
Heather Kulik Massachusetts Institute of Technology (MIT)
M L
Michael Lindsey University of California, Berkeley (UC Berkeley)
J L
Jianfeng Lu Duke University
S R
Santiago Rigamonti Humboldt-Universität zu Berlin
T S
Tess Smidt Massachusetts Institute of Technology (MIT)
B S
Berend Smit EPFL
A T
Alexandre Tkatchenko University of Luxembourg
J W
Jonathan Weare New York University

Registration

Back to top

IMSI is committed to making all of our programs and events inclusive and accessible. Contact to request accommodations.

In order to register for this workshop, you must have an IMSI account and be logged in. Please use one of the buttons below to login or create an account.