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.

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.

Michele Parrinello received his Laurea in physics from the University of Bologna in 1968. After working at the International School for Advanced Studies in Trieste, the IBM research laboratory in Zurich, and the Max Planck Institute for Solid State Research in Stuttgart, he was appointed Professor of Computational Science at the Swiss Federal Institute of Technology Zurich in 2001, a position he also holds at the Università della Svizzera italiana in Lugano. In 2004 he was elected to Great Britain’s Royal Society. In 2011 he was awarded the Marcel Benoist Prize. Between 2014 and 2018, he was a member of the Scientific and Technical Committee of the Italian Institute of Technology (IIT). Since 2018, he has been a Senior Researcher, and since 2020, the Principal Investigator of the Atomistic Simulations research unit at the Italian Institute of Technology (IIT). In 2020 he received the Benjamin Franklin Medal (Franklin Institute) in Chemistry. As of 2024, he has received over 150,000 scientific citations and has an h-index of 163, which is one of the highest among all scientists.
Over the last four decades, he has introduced many groundbreaking simulation methodologies which greatly widened the applicability and scope of atomistic simulations. The first of these approaches is the 1981 Parrinello-Rahman method aimed at performing molecular dynamics at constant pressure with adjustable simulation cells. This method enabled the simulation of solid-solid phase transitions in materials, and it is still widely used to this date. Simulations carried out around that time were based on empirical models for the interatomic interactions, a feature that limited their general applicability and predictive power. For this reason, in 1985, together with Prof. Roberto Car, he developed ab initio molecular dynamics (now known as the Car-Parrinello method), a landmark technique based on driving the nuclear dynamics using forces calculated on-the-fly from quantum-mechanical electronic-structure calculations based on Density Functional Theory

Dr. Ramprasad joined the School of Materials Science and Engineering at Georgia Tech in February 2018. Prior to joining Georgia Tech, he was the Centennial Term Professor of Materials Science and Engineering at the University of Connecticut. He joined the University of Connecticut in Fall 2004 after a 6-year stint with Motorola’s R&D laboratories at Tempe, AZ. Dr. 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’s area of expertise is in the development and application of computational and machine learning tools to accelerate materials discovery, as applicable to energy production, storage and utilization

Trevor David Rhone received a liberal arts education from Macalester College in Saint Paul. He went on to pursue his doctoral studies at Columbia University in the city of New York where he did experimental studies of two-dimensional electron systems in the extreme quantum limit. Trevor David spent several years at NTT Basic research laboratories in Japan. During a research stint at the National Institute of Materials Science in Japan, he transitioned to materials informatics research - exploiting machine learning tools to perform materials research. He continued this work at Harvard University where he used machine learning tools to search for new 2D magnetic materials.
Trevor David Rhone's research interests involve using machine learning tools for materials discovery and knowledge discovery. Materials discovery could manifest in the search new 2D materials with exotic properties, the prediction of the outcome of industrially relevant catalytic reactions or for other compelling research problems. In addition, data analytics tools will be used to aid in developing a better understanding of physical systems.

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 a Full Professor and Fellow of the Royal Society of Chemistry (FRSC) in the Department of Materials. He leads the Materials Design Group within the Thomas Young Centre. He is Research Area Lead for Modelling & Simulation at the Henry Royce Institute and has served as an Associate Editor for the Journal of the American Chemical Society (JACS) covering artificial intelligence.
Aron was awarded his PhD in Chemistry from Trinity College Dublin. He subsequently worked for the US Department of Energy at the National Renewable Energy Laboratory, followed by a Marie Curie Fellowship hosted by University College London, and a Royal Society University Research Fellowship at the University of Bath.