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Technische Universität München
Technische Universität München

MDSI Workshop: Computational Material Design powered by Machine Learning

Date: 24. February, 2022

Abstract

Program

Registration

Abstract

Computational science has been of paramount importance in providing physical understanding as well as technological advancement for material and device development. Recently, the field has been propelled by the fusion of computational and machine learning (ML) techniques. Several approaches were proposed ranging from directly modeling atomistic structure-property relations, using ML as a surrogate model, to incorporating directly ML models within simulations. Although different spatial scales in materials (quantum, atomistic, continuum) face different obstacles, many challenges are common to all scales such as the need for sufficiently broad datasets that are simultaneously as small as possible. Knowledge transfer between computational science subfields can therefore boost advances in both method development and application.

The workshop aims at bringing together leading world and local TUM experts in the field to foster collaborations, initiate discussion, and stimulate new ideas to tackle the pressing challenges in computational material design. 

 

Program

09:00 - 09:15

Welcome by Organizers
Minisymposium 1 (moderator: Alessio Gagliardi)
09:15 - 09:50

Keynote lecture - Michele Ceriotti (EPFL)
Machine learning for atomistic materials modeling
09:50 - 10:00

Break
10:00 - 10:20

Short Talk - Stelios Koutsourelakis
Data-driven inversion of the process-structure-property chain for the design of random material microstructures
10:20 - 10:40

Short Talk - Stephan Günnemann
Directional Graph Neural Networks for Molecules
10:40 - 11:00

Short Talk - Julija Zavadlav
Machine learning-based molecular modeling consistent with experimental data
11:00 - 13:00

Lunch Break
Minisymposium 2 (moderator: Stephan Günnemann)
13:00 - 13:35

Keynote lecture - Frank Noe (FU Berlin)
Deep Learning for Molecular Physics
13:35 - 13:40

Break
13:40 - 14:00

Short Talk - Harald Oberhofer
Computational Design of Small Molecule Organic Semiconductors
14:00 - 14:20

Short Talk - David Egger
Raman Spectra of Materials with Kernel-Based Machine Learning
14:20 - 14:40 Short Talk - Alessio Gagliardi

Multiscale simulations & Machine learning: charge transport in organic semiconductors
14:40 - 15:00

Break
Minisymposium 3 (moderator: Julija Zavadlav)
15:00 - 15:20 Short Talk - Nils Thuerey
Deep Learning Algorithms for Fluid Simulations
15:20 - 15:40 Short Talk - Nikolaus Adams

Multi-fidelity prediction of crystallization as basis of Bayesian optimization
15:40 - 16:00

Short Talk - Axel Zimmermann
What have we learned about combustion dynamics?
16:00 - 16:05 Break
16:05 - 16:40

Keynote lecture - Petros Koumoutsakos (Harvard)

AI and Computational Science: Computational Intelligence vs Artificial Science
16:40 - 16:50

Closing words
   
17:15 - 18:15

Closed session at MDSI

 

Registration

Please register your participation via the following link:

https://tum-conf.zoom.us/meeting/register/u5crcOqsqTgjHdIgL8l-umv4w1I0Xa-OYhHK

After registering, you will receive a confirmation email containing information about joining the meeting. Registration is open until 20. February, 2022.

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