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.

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.

As well as being Head of Molecular Engineering at Cambridge, Jacqueline Cole is the Cambridge lead for two of the EPSRC-funded UK AI Hubs, AIchemy and APRIL, where she is accelerating data-driven materials discovery using AI for chemistry and electronics, respectively.
She combines artificial intelligence with data science, machine-learning algorithms, computational methods and experimental research to afford a ‘design-to-device’ pipeline for data-driven materials discovery.
She is particularly well known for provisioning the global research community with open-access materials databases of experimental information, machine-learning code and models for property prediction and language models that are tailored for the materials domain.
Her research is highly interdisciplinary. Accordingly, she holds two PhDs: one in Physics from the University of Cambridge and one in Chemistry from the University of Durham.
Before moving to Cambridge, she held a post-doctoral position in Physics at the University of Kent at Canterbury, UK. Prior to this, she undertook a PhD in Chemistry through an international studentship between the Institute Laue Langevin, Grenoble, France, and Durham University. Her university studies began at Durham University where she graduated with first class honours in Chemistry in 1994.

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.

Dr. Payel Das is a Principal Research Staff Member, an IBM Master Inventor, and a manager in the Trusted AI Department of IBM Thomas J Watson Research Center in Yorktown Heights, NY. She received her Ph.D. degree from Rice University, Houston in 2007, where her thesis focused on statistical physics and machine learning. Her research interest is at the interface of artificial intelligence (AI) and natural sciences (physics, biology, chemistry, and neuroscience).
In her current role, Das leads research on trustworthy generative AI systems and neuro-inspired novel AI architectures, which are efficient, safe and grounded. She also manages the partnership between IBM and U Montreal as an AI Horizon Network Principal Investigator. Das has served in the editorial advisory board of the ACS Central Science journal, in the editorial board of the Machine Learning: Science and Technology journal, and in the SUNY Stony Brook Advisory Board. She was also an adjunct associate professor at the department of Applied Physics and Applied Mathematics (APAM), Columbia University 2019-2021. She has co-authored over 50 publications and several patent disclosures, and has given dozens of invited talks.

Dr. Tim Erdmann is a Staff Research Scientist at IBM Research - Almaden. His primary research interests currently focus on developing software applications that leverage generative AI and large-language models for the domain of chemistry to democratize access to expert tools and AI models.
Dr. Erdmann holds a PhD in Polymer Chemistry from TU Dresden/CFAED (Cluster of Excellence ‘Center for Advancing Electronics Dresden’) with specialization in synthesis and characterization of semiconducting polymers and joined IBM Research end of 2017 through a Feodor Lynen Postdoctoral Research Fellowship of the Humboldt foundation. In early 2019 while working on conductive polymer-based sensors for VOCs, he discovered his passion for programming and since then followed a self-guided learning path while working with Dr. Jim Hedrick and the team on organocatalytic polymerizations in flow reactors, carbonate monomer synthesis, upcycling of CO2, and automated sol-gel synthesis partly involving AI model training. Since Spring 2023 Tim leads the project IBM ChemChat, an LLM-powered and cloud-native conversational assistant for material science and data visualization deployed on IBM Cloud.

I am 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. My research area is Artificial Intelligence with a focus on large-scale constraint-based reasoning, optimization, and machine learning. Recently, I have become deeply immersed in the establishment of the new field of Computational Sustainability and in AI for 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.

I am the Scientific Director (CSO) and Cofounder of Multiverse Computing, as well as Ikerbasque research professor at the Donostia International Physics Center (DIPC) in San Sebastián, Spain. After obtaining my degree and PhD in Physics at the University of Barcelona in 2006, I worked as a research fellow at the University of Queensland, Australia, and the Max Planck Institute of Quantum Optics, Germany, as well as a junior professor at Johannes Gutenberg-Universität in Mainz, Germany. I was also visiting professor at the Universitè Paul Sabatier – CNRS, France, and at the DIPC. My research has been recognized by several awards, including a Marie Curie Incoming International Fellowship, and the Early Career Prize (2014) by the European Physical Society. I have written more than 80 scientific articles about quantum research cited around 5500 times, and I am also honorary member of the steering board of the journal Quantum, member of the ‘Quantum for Quants’ (Q4Q) commission of the Quantum World Association, partner at Entanglement Partners, and president of the Specialized Group on Quantum Information at the Spanish Royal Society of Physics.

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) . 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. Milica gained an MSci in Physics at University College London, followed by a DPhil in Materials Science at the University of Oxford. She 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, Milica teamed up with computer science partners at the Finnish Center for AI, where she is co-lead of Highlight E: AI-driven Design of Materials. With a record of cross-disciplinary collaborations and multi-modal AI applications to numerical data, scientific texts and microscopy images, the MIL group seeks to disseminate AI across natural sciences, engineering and industry.

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.