Piero Altoe is Senior Developer Relations Manager for Computational Chemistry and Materials Science at NVIDIA. He earned a PhD in Computational Chemistry from the University of Bologna in 2007, specializing in multiscale simulation. After more than a decade in academic and industrial research, he transitioned to high-performance computing, promoting GPU adoption across Europe. His expertise includes molecular dynamics, free-energy methods, machine-learning interatomic potentials, and scientific software optimization, working closely with academia and industry to accelerate scientific applications on modern architectures.

Dr. Elmar Bonaccurso is a technology eXpert for Functional Surfaces in the Airbus Central R&T Materials Technology Department. Apart from his daily activities as a materials technologist, he is also managing & operating a small-scale icing wind tunnel facility for aircraft icing research with supercooled droplets and ice crystals. Elmar previously held positions of Research Group Leader @ Center of Smart Interfaces of TU Darmstadt and of Group Leader @ Max Planck Institute for Polymer Research in Mainz. Elmar was affiliate Professor @ Department of Mechanical, Industrial & Aerospace Engineering of Concordia University in Montreal (2017-2022), he was member of the Executive Board of the Graphene Flagship (2019-2023), and since 2025 he is member of the Executive Board of the Innovative Advanced Materials Initiative. He is co-founder of two start-up companies, Aeomiq and SurfLabX.

Keith Butler is an Associate Professor at UCL Chemistry, specializing in data-driven materials discovery and optimisation. Before UCL he was based at Queen Mary University and the Rutherford Appleton Laboratory, where he was one of the founding members of the Scientific Machine Learning team. He is PI of the Materials Design and Informatic Group (https://mdi-group.github.io/) which works with collaborators from academia, national facilities and industry to design and optimise new materials. His work with industry was recently awarded the Sir George Stokes Prize from the RSC. He is deputy editor of npj Computational Materials and sits on the editorial board of Machine Learning Science and Technology. Keith is a strong advocate for open science and responsible innovation, he contributes to several community-developed computational tools.

Maria Chan is a scientist at the Center for Nanoscale Materials at Argonne National Laboratory who studies nanomaterials and renewable energy materials, including solar cells, batteries, thermoelectrics, and catalysts. Her particular focus is on using artificial intelligence/machine learning (AI/ML) for efficient materials property prediction and for interfacing modeling with x-ray, electron, and scanning probe characterization. She also works on using AI for extracting microscopy and spectroscopy data from scientific literature and for microscopy data management.

Jacqueline Cole is a Professor of Materials Physics at the Cavendish Laboratory, Department of Physics, University of Cambridge. She holds the post concurrently with the Henry Royce Institute for whom she is the Challenge Lead in AI for Materials Discovery, Characterisation and Application

Gabor Csanyi is Professor of Molecular Modelling in the Engineering Laboratory at the University of Cambridge. After a degree in mathematics at Cambridge and a PhD in computational physics at MIT, he did a postdoc in the Cavendish Laboratory before taking up a faculty position in Engineering. He has been working on applying machine learning to quantum mechanics for 15 years, focussing on chemical representations, encoding symmetries, and force fields - originally for materials and more recently for organic molecules.

Professor Andrés Díaz Lantada is an Industrial Engineer (2005) and holds a Ph.D. in Mechanical Engineering and Manufacturing (2009) from Universidad Politécnica de Madrid (UPM). His teaching and research work has been conducted at the Department of Mechanical Engineering of UPM, where he was appointed Assistant Professor in 2007, Associate Professor in 2011, Associate Professor (Civil Servant) in 2020, and Chair Professor of Mechanical Engineering in 2025. He is a founding member of the Machines Engineering Research Group (2007) and the Educational Innovation Group for Innovative Machines Engineering Education (2006), which he has led since 2015. Since 2015, he has been the Director of the Product Development Laboratory at UPM, one of the pioneering laboratories in digital design and manufacturing worldwide, founded in 1997 by Professor Pilar Lafont (his mentor in the field of engineering) as a precursor to what would later be known as “FabLabs”. In 2025, he joined the IMDEA Materials Institute, maintaining his affiliation with UPM, to establish a research line in “Bioinspired, Smart, and Living Materials”.

Carla Gomes is the Ronald C. and Antonia V. Nielsen Professor of Computing and Information Science, the director of the Institute for Computational Sustainability at Cornell University, and co-director of the Cornell University AI for Science Institute. Gomes received a Ph.D. in computer science in artificial intelligence from the University of Edinburgh. Her research area is Artificial Intelligence with a focus on large-scale constraint reasoning, optimization, and machine learning. Recently, Gomes has become deeply immersed in research on scientific discovery for a sustainable future and, more generally, in research in the new field of Computational Sustainability. Computational Sustainability aims to develop computational methods to help solve some of the key environmental, economic, and societal challenges to help put us on a path toward a sustainable future. Gomes was the lead PI of two NSF Expeditions in Computing awards. Gomes has (co-)authored over 200 publications, which have appeared in venues spanning Nature, Science, and a variety of conferences and journals in AI and Computer Science, including several best paper awards. Gomes was named the “most influential Cornell professor” by a Merrill Presidential Scholar (2020). Gomes was also the recipient of the Association for the Advancement of Artificial Intelligence (AAAI) Feigenbaum Prize (2021) for “high-impact contributions to the field of artificial intelligence, through innovations in constraint reasoning, optimization, the integration of reasoning and learning, and through founding the field of Computational Sustainability, with impactful applications in ecology, species conservation, environmental sustainability, and materials discovery for energy” and of the 2022 ACM/AAAI Allen Newell Award, for contributions bridging computer science and other disciplines. Gomes is a Schmidt AI2050 Senior Fellow, a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), a Fellow of the Association for Computing Machinery (ACM), and a Fellow of the American Association for the Advancement of Science (AAAS).

Roberto Iglesias has been an associate professor at the Department of Physics of the University of Oviedo since 2008. His current research focuses on multiscale materials modelling, combining DFT and MD simulations with machine learning and artificial intelligence methodologies. He has been very active in tackling structural, diffusive, and magnetic materials properties, especially in connection to nuclear fusion reactors scenarios. He has served in diverse roles in numerous national and European projects, including CE, COST and EIT-RM initiatives, leveraging his supercomputational skills. He leads the CIMACO research group at his university and supervises PhD, master and degree students while teaching advanced materials simulations courses, with focused content on computational screening, inverse design and machine learning in materials science.

Yousung Jung is a Professor of Chemical and Biological Engineering at Seoul National University. His research background and current interests involve quantum chemistry and machine learning to develop efficient methods for fast and accurate simulations of complex molecular and materials systems and their applications toward the understanding of molecules and materials for new discovery. Some of his recent works include using data science and machine learning to understand the structure-property-synthesizability relations for molecules and materials and using the obtained knowledge for inverse design. He received his PhD in Theoretical Chemistry from the University of California, Berkeley, with Martin Head-Gordon. After postdoctoral work at Caltech with Rudy Marcus, he joined the faculty at KAIST in 2009 and recently moved to Seoul National University in 2023. He has received the following awards: the Hanseong Science Award from Hanseong Son Jae Han Foundation; the KAIST Technology Innovation Award; the Pole Medal by the Asia-Pacific Association of Theoretical and Computational Chemists; a Korean Chemical Society Young Physical Chemist Award, and a KCS-Wiley Young Chemist Award.

My research focuses on the theory and simulation of molecular reactions on surfaces and in materials. I study the structure, composition, and reactivity of molecules interacting with solid surfaces. Our goal is to find a detailed understanding of the explicit molecular-level dynamics of molecular reactions as they appear in catalysis, photochemistry, and nanotechnology. Members of my research group develop and use electronic structure theory, quantum chemistry, molecular dynamics, and machine learning methods to achieve this.

After a long carrier during which he has covered many important positions in Academe and Industry, Michele Parrinello now leads the Atomistic Simulation group at the Italian Institute of Technology in Genoa Italy. He is known for many innovations in the field atomistic simulations, the most famous being the development of the ab-initio molecular dynamics method. More recently he has pioneered the application of machine learning methods to atomistic simulations. He has been awarded numerous prizes, and is fellow of several of the most prestigious academies. His work is highly cited with a Scopus h- index of 147.

Prof. Ramprasad is the Regents’ Entrepreneur, Michael E. Tennenbaum Family Chair and Georgia Research Alliance Eminent Scholar in the School of Materials Science & Engineering at the Georgia Institute of Technology. His area of expertise is the development and application of computational and machine learning tools to accelerate sustainable materials development aimed at energy production, storage and utilization. He is also the Founder of Matmerize, Inc., a company that offers AI-based software solutions to help accelerate polymers and formulations development. Prof. Ramprasad received his B. Tech. in Metallurgical Engineering at the Indian Institute of Technology, Madras, India, an M.S. degree in Materials Science & Engineering at the Washington State University, and a Ph.D. degree also in Materials Science & Engineering at the University of Illinois, Urbana-Champaign.
Prof. Ramprasad is a Fellow of the Materials Research Society, a Fellow of the American Physical Society, an elected member of the Connecticut Academy of Science and Engineering, and the recipient of the Alexander von Humboldt Fellowship and the Max Planck Society Fellowship for Distinguished Scientists. He has authored or co-authored over 300 peer-reviewed journal articles, 8 book chapters and 8 patents, and has delivered over 300 invited talks at Universities and Conferences worldwide. He is a member of the Editorial Advisory Boards of npj Computational Materials, ACS Materials Letters and Journal of Physical Chemistry A/B/C. He created and chaired the inaugural 2022 Gordon Research Conference on Computational Materials Science and Engineering.

Trevor David Rhone received a liberal arts education from Macalester College, USA. He pursued his doctoral studies at Columbia University where he did experimental studies of two-dimensional electron systems using light scattering. Rhone moved to NTT Basic research laboratories in Japan where he received the BRL director award. While working at the National Institute of Materials Science in Japan, he transitioned to AI-guided materials discovery. He continued this work at Harvard University as a postdoctoral prize fellow where he used AI to search for new 2D magnets. Rhone is now a faculty member at RPI doing research at the intersection of materials science and AI. He received the NSF CAREER award and the Joseph A. Johnson award for research and mentoring.

Milica Todorović is an Associate Professor at the Department of Mechanical and Materials Engineering, University of Turku (Finland) . Following degrees from UCL and Oxford (UK), Milica went on to specialise in development and application of density functional theory applications to organic/inorganic materials and surfaces at the National Institute for Materials Science (Japan) and Universidad Autonoma de Madrid (Spain). In Finland, she leads the Materials Informatics Laboratory group, with a focus on interfacing artificial intelligence algorithms with computational and experimental materials data to accelerate materials discovery for energy, health and manufacturing applications.

Tejs Vegge is a Professor at DTU Energy, Technical University of Denmark, Director of the Pioneer Center for Accelerating P2X Materials Discovery (CAPeX), a member of the European Commission’s Technology Council for Advanced Materials, and vice-lead of Materials Commons for Europe. He develops machine learning and AI-enabled, transdisciplinary methods for the accelerated discovery of sustainable energy materials, including catalysts for sustainable fuels and chemicals, and next-generation battery materials. His work combines self-driving laboratories, density functional theory, machine-learned interatomic potentials and generative AI, and advanced characterization to understand and design materials under realistic operating conditions. He also coordinated the large-scale EU BIG-MAP project within BATTERY 2030+, helping pioneer materials acceleration platforms for advanced materials discovery. Prof. Vegge has been part of establishing three of Europe’s largest interdisciplinary consortia on developing Materials Acceleration Platforms (MAPs) and self-driving laboratories (SDLs) for accelerated materials discovery, i.e., CAPeX and the large-scale Battery Interface Genome–Materials Acceleration Platform (BIG-MAP) initiative under BATTERY 2030+, and most recently the MaterialsCommons for Europe

Aron Walsh is Professor of Materials Design at Imperial College London and Chief Scientific Officer at CuspAI. His research spans computational materials science, with expertise in electronic structure theory and machine learning for materials discovery. He was awarded the EU-40 Prize for his work on the theory of solar cells, and the RSC Corday-Morgan Prize for contributions to computational chemistry.