Interviews with Peter Haynes, Zoë Holmes, and Michele Ceriotti

We continue to publish the interviews filmed during the Psi-k conference in Lausanne in August 2025, where we asked top experts in the discipline to reflect on the state of the art and the future of computational materials science. This time we present the interviews to Peter Haynes, Zoë Holmes, and Michele Ceriotti. 

In his interview, the Psi-k 2025 conference chair Peter Haynes notes how the community has made impressive progress in terms of reliability and interoperability of the software tools available for simulating materials. But simulation methods – even those based on density functional theory, that owes its success to a good trade-off between accuracy and computational load  – remain computationally expensive. “We’re only scratching the surface” of what we can achieve using artificial intelligence to improve that trade off, says Haynes, who also notes that collaboration between academia and industry on computational materials is still a work in progress.

Peter is Professor of Theory and Simulation of Materials at Imperial College London. His research interests focus on the development of new methods for performing first-principles quantum-mechanical simulations and their application to materials science, nanotechnology and biological systems.

Applications of quantum computing and quantum machine learning to materials are hot areas of research surrounded by great expectations. Zoë Holmes, a group leader in the quantum simulation project in MARVEL, explains that whereas quantum hardware has seen impressive advancements over the last decade, software still needs to catch up. “We have a whole zoo of algorithms now, but we don't have that many end to end analysis which use different algorithmic primitives showing that things can work”, she says. As for quantum machine learning, in principle it has a great potential for studying materials, but so far most results have happened in niche areas. Her top tip for young scientists is to strive for originality, and try to introduce new algorithms rather than tweaking what others have done.

Zoë Holmes received in 2015 her MPhil degree in Physics and Philosophy from the University of Oxford. In 2016 she obtained her MRes (Master of Research) from the Imperial College London, where in 2019 she got her PhD in quantum thermodynamics. In 2020 she started as a Postdoctoral Researcher at Los Alamos National Laboratory (USA) working on quantum algorithms and quantum machine learning methods for Noisy Intermediate-Scale Quantum (NISQ) computers. In 2021 she became the Mark Kac Fellow at Los Alamos National Lab. Since August 2022 she is Tenure Track Assistant Professor of Physics at EPFL.

Michele Ceriotti calls the impact of machine learning on the field “revolutionary”, and says that machine learning not only speeds up calculations, but can actually bring improved understanding of quantum systems by allowing scientists to manipulate parts of their models to test hypothesis. One big challenge for the future, he says, is that the community has learned different flavours of electronic structure approximations that work for transition metals or transition metal oxides or or for reactions in the gas phase, but a single framework that is practical and can be applied across the board is still lacking. and without these reference data, something that limits the usefulness of universal machine learning models. As for the impact of MARVEL, Ceriotti says it mainly lies in the software infrastructure. “Even though it will not be trivial to keep maintaining these software now that MARVEL comes to an end, I think that it has definitely created a huge amount of value” he says.

Michele Ceriotti has been a professor at the department of Materials Science at EPFL since autumn 2013, establishing the Laboratory of Computational Science and Modeling (COSMO). Within MARVEL phase I, he was a group leader in Horizontal Project 4. Then in phase II, he has been the project leader of Design & Discovery Project 1 and also a group leader in Design & Discovery Project 2 and Incubator Project 2. Within MARVEL phase III, since May 2022, he is project leader of Pillar 2, Machine Learning Platform for Molecules and Materials. He is also one of the deputy directors of the NCCR MARVEL and member of the Executive Committee.