I am an expert in the first-principles simulation of electronic excitations and optical properties of complex materials. My team works in close collaboration with experimental groups on groundbreaking topics in condensed matter physics and optics, but we also engage in the development of theories, methods and scientific software. We use computational material modeling to search for improved electronic and quantum materials and to understand how they can be combined to create heterostructures, alloys, doped crystals or simply new compounds. My studies contribute to the exciting research field of computational materials design, an emerging multidisciplinary topic that brings together innovative work from condensed matter physics, materials chemistry and computer science.

Miguel Caro is originally from a small town (Cartaya) in southwestern Spain. He graduated with a Physics degree from University of La Laguna, Tenerife, Spain. He then moved to Cork, Ireland, where he pursued a PhD in computational condensed-matter physics under Prof. Eoin O’Reilly at the Tyndall National Institute. His thesis work, for which he was awarded his PhD in 2013, focused on theory of III-N alloys, a material system widely used for optoelectronic applications. After the PhD, he moved to Aalto University, Finland as a postdoc in 2013. In 2017 he obtained the Academy of Finland Postdoctoral Researcher grant and since 2020 he is Academy of Finland Research Fellow. Since 2023 he is Senior Scientist at the Department of Chemistry and Materials Science, Aalto University, and leads the Data-driven Atomistic Simulation (DAS) group. Dr. Caro’s current research interests concern the atomistic simulation of real materials, especially carbon-based materials, using a battery of simulation tools and methodologies, from density functional theory to machine learning.

Michele Ceriotti received his Ph.D. in Physics from ETH Zürich. He spent three years in Oxford as a Junior Research Fellow at Merton College. Since 2013 he leads the laboratory for Computational Science and Modeling, in the institute of Materials at EPFL, that focuses on method development for atomistic materials modeling based on statistical mechanics and machine learning. He is one of the core developers of several open-source software packages, including http://ipi-code.org and http://chemiscope.org, and proudly serves the atomistic modeling community as an associate editor of the Journal of Chemical Physics, as a moderator of the physics.chem-ph section of the arXiv, and as an editorial board member of Physical Review Materials.

Ekin Dogus Cubuk is a researcher at Google DeepMind where he works on deep learning and its applications to solid state physics and materials science. He received his Ph.D. from Harvard University where he studied the physics of disordered solids and battery materials using density functional theory and machine learning. After a brief postdoc at the Materials Science Department of Stanford University, he joined Google Brain in 2017. Since then, he has been studying the scaling and out-of-domain generalization properties of large neural networks, and their applications to materials discovery for applications including clean energy and information processing.

Marivi Fernández-Serra is a professor of physics and has been at Stony Brook University since 2008. She received her PhD in 2005 from the University of Cambridge. After this she worked as a postdoc at CECAM (Center for Atomic and Molecular Simulations) in Lyon, France.

Yousung Jung is a Professor of Chemical and Biological Engineering at Seoul National University. He received the Ph.D. in Theoretical Chemistry from University of California, Berkeley. After a postdoctoral work at Caltech, he joined the faculty at KAIST in 2009. He then moved to SNU in 2023. His research interests involve computational chemistry and machine learning to understand and design novel molecules and materials with desired properties. One of the main current interests is to predict the synthesis of molecules and materials based on data and machine learning. He is the recipient of Shin Kook Joe Academic Award (2023, Korean Chemical Society), Hanseong Science Award (2021, Hanseong Son Jae Han Scholarship Foundation), Pole Medal (2018, Asia-Pacific Association of Theoretical and Computational Chemists), Korean Chemical Society Young Physical Chemist Award (2017), Chemical Society of Japan Distinguished Lectureship Award (2015), and KCS-Wiley Young Chemist Award (2013).

Kristin Persson uses atomistic and first-principles computational methods coupled with high-performance computing technology and machine learning to advance materials for clean energy production and storage. She is also the Director of the Molecular Foundry, a DOE user facility for nanoscale science, and the Director of the Materials Project (www.materialsproject.org), a multi-national effort to compute the properties of all inorganic materials and provide the data and associated analysis algorithms free of charge. The ultimate goal of the initiative is to drastically reduce the time needed to invent new materials to serve societal needs, in particular advancing clean energy solutions. Within the Persson group, we leverage the software and data infrastructure of the Materials Project together with our expertise in materials informatics to study the physics and chemistry of materials particularly for energy production and storage applications. Current focus areas include design of novel photocatalysts, multi-valent battery electrode materials, Li-ion battery electrode materials, polar materials and liquid electrolytes for Li energy storage solutions and beyond.

G.-M. Rignanese is Professor at the Ecole Polytechnique de Louvain (EPL) and Research Director at the F.R.S.-FNRS. He received his Engineering degree from the Université catholique de Louvain in 1994 and Ph.D. in Applied Sciences from the Université catholique de Louvain in 1998. During his Ph.D., he also worked as a Software Development Consultant for the PATP (Parallel Application Technology Project), collaboration between CRAY RESEARCH and Ecole Polytechnique Fédérale de Lausanne (EPFL) in the group of Prof. Roberto Car. He carried his postdoctoral research at the University of California at Berkeley in the group of Prof. Steven Louie. In 2003, he obtained a permanent position at the Université catholique de Louvain. In 2022, he was appointed as Adjunct Professor at the Northwestern Polytechnical University in Xi'an (China). In 2019, he was named APS Fellow for original efforts developing free license software in the field of electronic structure calculations, and high-throughput calculations in a broad range of materials types.

Dr. Matthias Troyer is accountable for architecting Microsoft’s quantum computer and applications. His work at Microsoft is focused on accelerating scientific discovery globally by bringing to bear the benefits of a scaled, fault tolerant quantum system to the world ins secure and responsible ways. He received his PhD from ETH Zurich in Switzerland in 1994. Afterwards, he spent time as a post-doctoral fellow at the University of Tokyo, then returned to ETH Zurich as a Computational Physics professor. He joined Microsoft in 2017. Dr. Troyer is also a Fellow of the American Physical Society and President of the Aspen Center for Physics. He is the recipient of the Hamburg Prize for Theoretical Physics and the Rahman Prize for Computational Physics of the American Physical Society “… for pioneering numerical work in many seemingly intractable areas of quantum… physics and for providing efficient sophisticated computer codes to the community.”