Interviews with Giulia Galli and Georg Kresse

What will the future of computational materials science look like? What challenges does the field face, and how can the progress of the last ten years be sustained? What are the best moves for young researchers who are now approaching materials simulation? 

These are some of the questions that we asked to a group of top experts in theory and simulation of materials — some MARVEL members, some members of the wider global community — when we met them at the Psi-k conference that was held in Lausanne from 25 to 28 August 2025. The interviews, that will be published on the Materials Cloud YouTube channel and on the MARVEL website over the course of the next few weeks, cover some of the key topics that have inspired MARVEL over the last 12 years, and that will continue to inspire the work of scientists in the future.

The series starts with the interviews to Giulia Galli and Georg Kresse.

Giulia Galli is the Liew Family Professor of Electronic Structure and Simulations in the Pritzker School of Molecular Engineering and the Department of Chemistry at the University of Chicago. She also holds a senior scientist position at Argonne National Laboratory, where she is a group leader and the director of the Midwest Integrated Center for Computational Materials. She is an expert in the development of theoretical and computational methods to predict and engineer material and molecular properties from first principles. Her research focuses on problems relevant to the development of sustainable energy sources and quantum technologies. She is a member of the NCCR MARVEL Scientific Advisory Board. 

In the interview she reflects on how the progress of computational materials science over the last decades has exceeded her expectations. "The field has made a lot of progress towards open science, and this has pushed the community very far in terms of applying the calculations to realistic systems. And there has been a lot of development of new methods". But she also notes how the collaboration with experimentalists and with the industry can be improved, and she warns AI and machine learning should be applied to materials simulation with caution. "We need metrics to understand if we really remain predictive when we apply AI methods". 

Georg Kresse heads the Computational Materials Physics group at the University of Vienna. In addition to his research contributions, he is known in the computational materials science community for developing the Vienna ab initio simulation package (VASP), which is the leading code for first-principles calculations of solids and liquids and is used by 4,000 research groups worldwide. His group’s current is on modern quantum field theory and quantum chemistry methods, which allow more accurate predictions than density functional theory. 

Here, he takes stock of how machine learning is transforming the field and looks at the challenges that lie ahead for first-principle simulations, starting with accuracy. "We need to shift to more accurate methods that actually approximate the Schrödinger equation". He also warns young scientists who are entering the field of the perils of hyper-specialization. "Materials chemistry is one of the broadest fields that exist. You need thermodynamics, quantum mechanics, Maxwell equations. You need all this background information that you've learned in physics and you can forget very quickly when you start your PhD. So, broaden your knowledge".