Machine Learning for Polymer Science

Nicholas Ballard is an Ikerbasque Associate Professor at POLYMAT specialized in polymer chemistry, reaction engineering, and advanced polymerization processes, with a strong focus on data-driven approaches and machine learning applied to materials science. His research combines fundamental understanding of polymerization kinetics with the development of sustainable and efficient polymer production processes. He has extensive experience in European research projects, including coordination and participation in Horizon Europe and MSCA programmes, and plays a key role in international collaborative networks. His work has resulted in numerous high-impact scientific publications and contributions to the advancement of bio-based polymers and smart materials. At POLYMAT, he leads research activities related to polymerization modeling and process optimization, contributing to interdisciplinary projects at the interface of chemistry, engineering, and artificial intelligence.

Mónica Moreno is Head of the Project Office at POLYMAT. She holds a PhD in Chemistry and has solid expertise in the management of European research projects, particularly within Horizon Europe and MSCA programmes. She specializes in project coordination, financial and administrative management, and liaison with the European Commission, supporting the successful implementation of international collaborative research initiatives.

Alfred Bazin studied Chemistry and Polymer Science in Strasbourg, where he later completed a PhD at ICPEES focused on the synthesis and characterization of biobased polymers. After his doctorate, he specialized in machine learning with a particular emphasis on applications in polymer science. He is currently part of Arkema’s R&D Digital team, working as a Data Scientist and Digital Acculturation Scientist, where he advances data-driven approaches in R&D and leads initiatives to promote digital awareness and adoption across the organization.

Maciej Haranczyk received his PhD in Chemistry from the University of Gdańsk (Poland) in 2008. During his doctoral studies, he held several international research fellowships and collaborated with leading institutions, including Pacific Northwest National Laboratory, the University of Southern California, and the University of Sheffield. After his PhD, he joined Lawrence Berkeley National Laboratory as a Glenn T. Seaborg Postdoctoral Fellow, later becoming a Research Scientist and subsequently a Staff Scientist. In 2015, he joined IMDEA Materials Institute as a Senior Researcher under the Ramón y Cajal programme. He currently leads the Accelerated Materials Discovery Group and manages a robot-equipped polymer research laboratory focused on data-driven approaches for materials design.

Matthias Hermann is a Senior Solutions Engineer at Citrine Informatics. He holds a PhD in Analytical Chemistry from Queen’s University, where he focused on the development of microfluidic devices for automated mass spectrometry. He also has industrial experience as a lab team leader at BASF, where he led high-throughput screening projects for polymer formulations.

Usue Mori obtained a Bachelors degree in Mathematics and a PhD in Computer Engineering from the University of Basque Country UPV/EHU in 2010 and 2015 respectively. She completed a master's degree in Applied Mathematics, Statistics and Computing and a master's degree in Computer Engineering and Intelligent Systems in 2011 and 2013, respectively. Currently, she is an associate professor in the Department of Computer Science and Artificial Intelligence at the University of the Basque Country UPV/EHU and teaches various subjects in the field of mathematics, statistics and machine learning. As research merits, it should be noted that she has participated in more than 20 projects of regional, state and European calls, being IP in four of these projects. She has published 18 articles in JCR journals, 12 of them located in the first quartile and she also has 4 contributions in national and international conferences. She has also participated in 6 R&D contracts with companies of the private industrial and public sector, being IP the most recent. In addition, she has co-directed two doctoral theses and today she co-directs 3 doctoral theses with other researchers.

Thomas Nevolianis is a Cheminformatics Expert at Covestro’s Digital Research and Development in Leverkusen, Germany, where he supports R&D projects across the company’s global operations. He obtained his PhD in Chemical Engineering from RWTH Aachen University, following undergraduate studies in Physics. During his doctoral research, he held a visiting scholarship at MIT, specializing in molecular property prediction using machine learning and quantum chemical methods. At Covestro, his work focuses on computational approaches to polymer development, including the screening of monomers and additives for next-generation materials, prediction of polymer–solvent compatibility, and optimization of formulations. His research integrates graph neural networks, machine learning interatomic potentials, quantum mechanics, and molecular dynamics simulations to address regulatory, sustainability, and performance challenges. By translating computational methods into practical workflows, he contributes to accelerating materials discovery while reducing experimental time and costs.

Gregor Simm is a Senior Researcher and Project Lead at Microsoft Research, working within the AI for Science initiative. His research focuses on machine learning and atomistic simulation of matter, with strong interests in geometric deep learning, reinforcement learning, and generative modelling. From 2018 to 2021, he was a Research Associate in the Machine Learning Group at the University of Cambridge. He obtained his PhD in Theoretical Chemistry from ETH Zurich (2015–2018).