Combining machine learning methods and reactivity theories to better understand and analyze complex chemical processes
Research lines
Reaction screening and discovery
Molecular quantum mechanical modeling, accelerated by machine learning, has opened the door to high-throughput screening campaigns of chemical reactions, enabling the computational discovery of new catalysts, new bioorthogonal click reactions and new self-healing polymers. In this research line, we develop tools, approaches and strategies
Molecular quantum mechanical modeling, accelerated by machine learning, has opened the door to high-throughput screening campaigns of chemical reactions, enabling the computational discovery of new catalysts, new bioorthogonal click reactions and new self-healing polymers. In this research line, we develop tools, approaches and strategies to facilitate the set-up of these types of reaction discovery workflows, and we demonstrate how they can be put to productive use.
Selected publications:
- N. Casetti, J. E.. Alfonso‐Ramos, C. W. Coley, T. Stuyver, Combining molecular quantum mechanical modeling and machine learning for accelerated reaction screening and discovery, Chem. Eur. J. 2023, e202301957.
- M. Ferrer, B. Deng, J. Alfonso-Ramos, T. Stuyver, Screening Diels-Alder reaction space to identify candidate reactions for self-healing polymer applications. ChemRxiv 2025, DOI: 10.26434/chemrxiv-2025-kv6n0
- T. Stuyver, C. Coley, Machine Learning‐Guided Computational Screening of New Candidate Reactions with High Bioorthogonal Click Potential, Chem. Eur. J. 2023, e202300387.
Quantum chemistry-augmented neural networks
Traditional machine learning algorithms tend to require lots of training data. However, by introducing domain knowledge, one can dramatically improve the data efficiency -- as well as generalizability -- of these models. In this research line, we aim to augment models with descriptors stemming from quantum chemical calculations to facilit
Traditional machine learning algorithms tend to require lots of training data. However, by introducing domain knowledge, one can dramatically improve the data efficiency -- as well as generalizability -- of these models. In this research line, we aim to augment models with descriptors stemming from quantum chemical calculations to facilitate their application to domains for which only limited data is available.
Selected publications:
- T. Stuyver, C. Coley, Quantum chemistry-augmented neural networks for reactivity prediction: Performance, generalizability, and explainability, J. Chem. Phys. 2022, 156, 084104.
- T. Stuyver, C. Coley, Machine Learning‐Guided Computational Screening of New Candidate Reactions with High Bioorthogonal Click Potential, Chem. Eur. J. 2023, e202300387.
- J. E. Alfonso-Ramos, R. Neeser, T. Stuyver, Repurposing quantum chemical descriptor datasets for on-the-fly generation of informative reaction representations: application to hydrogen atom transfer reactions, Digit. Discov. 2024, 10.1039/D4DD00043A
Reaction network exploration
Many processes studied in transition-metal catalysis, bio- and polymer chemistry, enzyme catalysis and environmental chemistry involve a multitude of elementary reaction steps and competing reaction pathways. In order to analyze, control and/or steer the outcome of generic, complex reaction networks, all the relevant chemical compounds an
Many processes studied in transition-metal catalysis, bio- and polymer chemistry, enzyme catalysis and environmental chemistry involve a multitude of elementary reaction steps and competing reaction pathways. In order to analyze, control and/or steer the outcome of generic, complex reaction networks, all the relevant chemical compounds and associated elementary reactions need to be identified. In this research line, we aim to combine efficient algorithms and machine learning approaches to efficiently explore and analyze such networks.
Selected publications:
- Z. Tu, T. Stuyver, C. Coley, Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery, Chem. Sci. 2023, 14, 226-244.
- T. Stuyver, TS‐tools: Rapid and automated localization of transition states based on a textual reaction SMILES input, J. Compute. Chem. 2024, 45, 2308-2317
Development of reactivity theories
Effective QM augmentation of machine learning models inherently requires a thorough understanding of the fundamental principles of chemical reactivity. In this research line, we aim to expand and unify existing reactivity theories (conceptual Density Functional Theory, Valence Bond Theory, Molecular Orbital Theory etc.) through considerat
Effective QM augmentation of machine learning models inherently requires a thorough understanding of the fundamental principles of chemical reactivity. In this research line, we aim to expand and unify existing reactivity theories (conceptual Density Functional Theory, Valence Bond Theory, Molecular Orbital Theory etc.) through consideration of various reactivity problems.
Selected publications:
- T. Stuyver, F. De Proft, P. Geerlings, S. Shaik, How do local reactivity descriptors shape the potential energy surface associated with chemical reactions? The valence bond delocalization perspective, J. Am. Chem. Soc. 2020, 142, 10102-10113.
- T. Stuyver, S. Shaik, Unifying conceptual density functional and valence bond theory: The hardness–softness conundrum associated with protonation reactions and uncovering complementary reactivity modes, J. Am. Chem. Soc. 2020, 142, 20002-20013.
- T. Stuyver, B. Chen, T. Zeng, P. Geerlings, F. De Proft, R. Hoffmann, Do diradicals behave like radicals?, Chem. Rev. 2019, 119, 11291-11351.