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Graeme Day is Professor of Chemical Modelling at the University of Southampton. His research concerns the development of computational methods for modelling the organic molecular solid state. A key focus of this work is the prediction of crystal structures from first principles; his research group applies these methods in a range of applications, including pharmaceutical solid form screening, NMR crystallography and computer-guided discovery of functional materials. 
 
After a PhD in computational chemistry at University College London, he spent 10 years at the University of Cambridge, where he held a Royal Society University Research Fellowship working mainly on modelling pharmaceutical materials and computational interpretation of terahertz spectroscopy. He moved to the University of Southampton in 2012, at which time he was awarded a European Research Council Starting Grant for the 'Accelerated design and discovery of novel molecular materials via global lattice energy minimisation' (ANGLE). This grant shifted the focus of his research to functional materials, including porous crystals and organic electronics. In 2020, he was awarded an ERC Synergy grant 'Autonomous Discovery of Advanced Materials' (ADAM) with Andrew Cooper (Liverpool) and Kerstin Thurow (Rostock) to integrate computational predictions, chemical space exploration with automation in the materials discovery lab.
 
Graeme has served on the editorial boards of CrystEngComm, Faraday Discussions and on the advisory board of Molecular Systems Design & Engineering (MSDE).

Leroy (Lee) Cronin FRSE is the Regius Professor of ¾ÅÖÝÓ°Ôº in Glasgow. Prizes include 2019 Japan Society of Coordination ¾ÅÖÝÓ°Ôº International Prize, 2018 ACS Inorganic Lectureship, 2018 RSC Interdisciplinary Prize, 2015 RSC Tilden Prize, 2013 BP/RSE Hutton Prize, 2012 RSC Corday Morgan, 2011, Election to the Royal Society of Edinburgh in 2009. His research has four main aims 1) the construction of an artificial life form / work out how inorganic chemistry transitioned to biology / searching for new life forms; 2) the digitization of chemistry; and 3) the use of artificial intelligence in chemistry including the construction of ‘wet’ chemical computers; 4) The exploration of complexity and information in chemistry. He runs a team of around 60 people funded by grants from the UK EPSRC, US DARPA, Templeton, Google, BAe, JM.

Dr. Jill S. Becker is CEO of Kebotix, a technology platform company for new AI-discovered chemicals & materials. Prior, Jill founded two successful tech companies, 02139 & Cambridge NanoTech. A past Ernst and Young Entrepreneur of the Year winner in energy & materials and a YPOer, Jill earned her PhD & MA in chemistry from Harvard. In her spare time, besides travelling, she loves to spend time with her friends & family. Like most chemists’ Jill is a foodie and deeply appreciates hauté gastronomy.

Kerstin Thurow (Member, IEEE) received the Habilitation degree in automation and control from the University of Rostock, Rostock, Germany, in 1999.
Since 2003, she has been the CEO of the Center for Life Science Automation, University of Rostock, where she has been holding the Chair of the Automation Technologies/Life Science Automation since 2004. She has authored more than 190 papers in journals and conferences. Her major research interests include life science automation, medical automation, mobile robotics, and automated analytical measurement.

Yousung Jung is a Professor of Chemical and Biomolecular Engineering at KAIST. He received the Ph.D. in Theoretical ¾ÅÖÝÓ°Ôº from University of California, Berkeley with Martin Head-Gordon. After a postdoctoral work at Caltech with Rudy Marcus, he joined the faculty at KAIST in 2009. His research interests involve electronic structure theory, statistical modeling, and machine learning to develop efficient methods for fast and accurate simulations of complex molecular and materials systems, and their applications towards the understanding and inverse design problems in chemistry and materials science. He is the recipient of 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).
 

Joshua Schrier is the Kim. B. and Stephen E. Bepler Professor of ¾ÅÖÝÓ°Ôº at Fordham University in New York City.  The central theme of his research is the use of computers to accelerate the discovery of new materials, using a combination of physics-based simulations, cheminformatics, machine learning, and automated experimentation; current projects focus on halide perovskites and amine-templated metal oxides as example materials.  He is also deeply committed to undergraduate chemistry education and is the author of the textbook, "Introduction to Computational Physical ¾ÅÖÝÓ°Ôº" (2017). 
 
Prof. Schrier received his doctoral degree in theoretical physical chemistry from the University of California, Berkeley (with K. Birgitta Whaley), and was the Alvarez Computational Sciences Postdoctoral Fellow at Lawrence Berkeley National Laboratory (with Lin-Wang Wang).  Prior to joining Fordham in 2018, he was on the faculty at Haverford College, where he served as ¾ÅÖÝÓ°Ôº Department Chair and coordinator of the Scientific Computing program.  He has received awards including the Dreyfus Teacher-Scholar award (2014) and U.S. Department of Energy Visiting Faculty Award (2017).

Professor Jacqueline Cole holds the Royal Academy of Engineering Research Professorship in Materials Physics at the University of Cambridge, where she is Head of Molecular Engineering. She concurrently holds the BASF / Royal Academy of Engineering Research Chair in Data-driven Molecular Engineering of Functional Materials. This is partly funded by the ISIS neutron and Muon Source, STFC Rutherford Appleton Laboratory, Oxfordshire, UK, with whom she holds a joint appointment. At Cambridge, she carries a joint appointment between the Physics Department (Cavendish Laboratory) and the Department of Chemical Engineering and Biotechnology at Cambridge.

Her research combines artificial intelligence with data science, computational methods and experimental research to afford a 'design-to-device' pipeline for data-driven materials discovery.

Her research has been recognised by the Royal Society Clifford Paterson Medal and Lecture 2020; the BASF / Royal Academy of Engineering Research Chair and Senior Research Fellowship in Data-driven Molecular Engineering of Functional Materials (2018-2023); the 1851 Royal Commission 2014 Fellowship in Design (2015-8), a Fulbright Award (all disciplines Scholar, 2013-4), an ICAM Senior Scientist Fellowship (2013-4); The Vice-Chancellor's Research Chair, University of New Brunswick, Canada (2008-2013), a Royal Society University Research Fellowship (2001-11), a Senior Research Fellowship (2002-2009) and Junior Research Fellowship (1999-2002) from St Catharine’s College, Cambridge, UK; the ¾ÅÖÝÓ°Ôº SAC Silver Medal and Lecture (2009); the Brian Mercer Feasibility Award (2007); the 18th Franco-British Science prize (2006); the first British Crystallographic Association Chemical Crystallography Prize (2000).

María José Nieves Remacha received her PhD (2014) and MS (2009) from Massachusetts Institute of Technology, where she worked with Klavs F. Jensen in scaling up multiphase continuous flow chemistries from micro to milli scales, from both an experimental and computational fluid dynamics perspective. After that she worked for The Dow Chemical Company in Core R&D (Freeport, TX), providing technical expertise in reaction engineering and revealing non-obvious process insights through modeling, optimization, and statistics.
In 2016, she joined Eli Lilly and Company (Spain) to work in the Discovery Flow ¾ÅÖÝÓ°Ôº Group and later in the Innovation and Technology Group implementing novel technologies, smart experimentation, and data analysis strategies to optimize processes in chemical synthesis. Her research interests are at the interface of computer science, engineering and chemistry, including: accelerating drug discovery through artificial intelligence and building laboratory automated systems with increasing level of autonomy.