Spring School Speakers

Speaker to be determined

Firstname Surname 2

Wednesday 10:30 - 11:30  

Introduction to AI and ML, basics, real life and research examples

Abstract: to be announced

Speaker to be determined

Firstname Surname 1

Wednesday 11:30 - 12:30  

Machine Learning, Catalysts, Energy Materials, Methods, Models

Abstract: to be announced

Nong Artrith

Firstname Surname 2

Wednesday 13:30 - 14:30  

Machine Learning Materials

Abstract: to be announced

Robert Pollice

R. Pollice portrait
Robert Pollice

Thursday 09:00 - 10:00  

Artificial Organic Chemistry

In this lecture, I will explain some of the applications of artificial intelligence in organic chemistry, with a particular focus on the design and synthesis of molecules.

For that, we will look into the three main branches of machine learning and explore the mathematical foundation of some of the most popular artificial neural networks. We will cover some of the most important representations of molecules in the context of machine learning, such as SMILES and SELFIES. Based on that foundation, I will introduce some of the algorithms we have developed that can be used for inverse molecular design. Finally, I will talk about the key concepts behind Artificial Intelligence approaches for reaction prediction and computational retro-synthesis.

Biography: In 2015, Robert received a Master of Science in Technical Chemistry, working with Professor Michael Schnürch on mechanistic investigations of C-H activation reactions. Subsequently, he moved to Switzerland to join the group of Professor Peter Chen at ETH Zurich for his doctoral studies investigating London dispersion in molecular systems. Afterward, he decided to work in the group of Professor Alán Aspuru-Guzik at the University of Toronto as a postdoctoral fellow to conduct research on organic electronic materials, lab automation and machine learning. In August 2022, Robert started as Assistant Professor for Computer-Aided Organic Synthesis at the University of Groningen.

Marc-Etienne Moret

Firstname Surname 2

Thursday 10:00 - 11:00

Computational chemistry in homogenous catalysis

Abstract: to be announced

Zeila Zanolli

Picture of dr. Zeila Zanolli
Zeila Zanolli

Thursday 11:00 - 12:00  

Quantum Materials by Design

Major advances in human civilization are driven by developments in materials. This is such a remarkable feature that historical eras are named after the material (and the related technology) that dominated that time. Today we live in the silicon era: Silicon technology enables our modern way of life via mobile phones, computers, automation. However, we are reaching the limits of silicon technology, as the related energy demand is not sustainable. It is time to move forward. As a scientist, we work to answer the question: what is the material that will enable the next revolution?

In this lecture, I will introduce quantum materials as a possible way forward. These are materials where quantum behavior is visible at macroscopic scales and, hence, can be used as a basis for developing quantum technologies.  Using the predictive power of first-principles techniques, I will explain how to understand, control and design quantum materials and how to exploit the quantum effects emerging at reduced dimensionality and at interfaces to achieve dissipationless (topological) carrier transport, tune charge and spin relaxation processes, or identify spectroscopic signatures of defects in 2D layered materials.

Biography: Zeila Zanolli is Associate Professor in Theory and Simulation of Quantum Materials at Utrecht University since 2020. She has over 20 years of experience in the study of quantum materials (Dirac materials, topological insulators, superconductors) using Density Functional Theory (DFT) to predict electronic, magnetic and topological properties, many-body techniques to address non-equilibrium quantum electron transport (Non-Equilibrium Green’s Function + DFT) and theoretical spectroscopy (GW approximation/Bethe-Salpeter Equation). She was among the first to demonstrate that magnetism and topological features can be controlled by proximity interaction at interfaces. Her group is among the developers of the SIESTA and YAMBO codes. In her career, she organized numerous international schools on Quantum Materials (EPS, U. Pisa), first-principles simulations (CECAM, MaX), and related conferences (EPS, ETSF). She serves in the board of the European Theoretical Spectroscopy Facility (2018 - present) and of the ”Semiconductors and Quantum Materials” section of the European Physical Society (2022 – present). She was elected fellow (2017) and board member (treasurer 2018-22) of the Young Academy of Europe.

Marjolein Dijkstra

Firstname Surname 2

Thursday 17:00 - 18:00

title to be announced

Abstract: to be announced

Nina Jeliazkova

Firstname Surname 1

Friday 09:00 - 10:00  

Data Management and eNanoMapper experience

Abstract: to be announced

Alexandru Telea

A. Telea portrait
A. Telea

Friday 10:00 - 11:00

Data Visualization: Seeing is learning in high dimensions

In this lecture: How I aim to provide an overview of how to visually explore high-dimensional data actionably and effectively by state-of-the-art methods, avoid errors or misconceptions when using such techniques, so domain experts (like chemists) can make the best use of recent advances in information visualization.

Multidimensional projections (MPs) are one of the techniques of choice for visually exploring large high-dimensional data, such as emerging from measurements or simulations in chemistry, physics, or similar fields. In parallel, machine learning (ML) and in particular deep learning applications are one of the most prominent generators of large, high-dimensional, and complex datasets which need visual exploration. In this talk, I will explore the connections, challenges, and potential synergies between these two fields. These involve “seeing to learn”, or how to deploy MP techniques to open the black box of ML models, and “learning to see”, or how to use ML to create better MP techniques for visualizing high-dimensional data. Specific questions I will cover include selecting suitable MP methods from the wide arena of such available techniques; using ML to create faster and simpler to use MP methods; assessing projections from the novel perspectives of stability and ability to handle time-dependent data; extending the projection metaphor to create dense representations of classifiers; and using projections not only to explain, but also to improve, ML models.

Biography: Alexandru Telea is Professor of Visual Data Analytics at the Department of Information and Computing Sciences, Utrecht University. He holds a PhD from Eindhoven University and has been active in the visualization field for over 25 years. He has been the program co-chair, general chair, or steering committee member of several conferences and workshops in visualization, including EuroVis, VISSOFT, SoftVis, and EGPGV. His main research interests cover unifying information visualization and scientific visualization, high-dimensional visualization, and visual analytics for machine learning. He has authored over 350 papers. He is the author of the textbook “Data Visualization: Principles and Practice” (CRC Press, 2014), a worldwide reference in teaching data visualization.