Current

Seminar

Multiscale Modeling with Machine Learning Potentials

AI for Science seminar with Julija Zavadlav, Technical University of Munich.

Add to OutlookAdd to Google Calendar

Overview

Zoom password: ai4science

Julija Zavadlav

The on-site event will be followed by fika in the Analysen coffee area (fika from 16:00-16:30).

Abstract:

Multiscale modeling is essential for understanding complex phenomena in fields ranging from life sciences to materials engineering. A prominent research area is the development of machine learning potentials (MLPs), particularly those based on Graph Neural Networks (GNNs), which have emerged as a powerful tool for bridging the gap between quantum-mechanical accuracy and classical molecular dynamics efficiency.

In this presentation, I will showcase the significant achievements of both atomistic and coarse-grained MLPs in effectively capturing many-body interactions. I will address the current challenges of MLP development, including the broad and accurate training dataset generation, capturing long-range interactions, and numerical stability.

To address these challenges, we propose a range of innovative strategies that encompass novel training objectives, the synergistic integration of diverse data sources, physics-based GNN architectures, and advanced Bayesian methods for uncertainty quantification. Through insightful case studies of various molecular systems, I will demonstrate the practical effectiveness and versatility of our approaches.

Lastly, I will introduce our software platform, chemtrain, designed to streamline the training of machine learning potentials with customizable routines and advanced training algorithms.

About the speaker:

Julija Zavadlav obtained her Ph.D. in physics at the University of Ljubljana in 2015. She joined ETH Zurich in 2016 for her postdoc and received an ETH Postdoctoral Fellowship award a year later.

In 2019, she was appointed as Assistant Professor for Multiscale Modeling of Fluid Materials at the Technical University of Munich. She was awarded with the ERC starting grant in 2022. Her research area is a combination of molecular modeling, multiscale simulations, and machine learning applied to complex phenomena ranging from life sciences to engineering.

 

Structured learning

This theme focuses on how to make use of structure in data to build machine learning (ML) and artificial intelligence (AI) systems which are safer, more trustworthy and generalize better. Structure includes the relationship between data, in time and space, and how the predictions change when data is transformed in specific ways, for example rotated or scaled. These topics are abstract and general but have a direct impact on the use of AI and ML in the sciences and in applications such as drugs and materials design, or medical imaging.