Teodoro Laino

IBM Zurich, CH



Teo received the Master degree in theoretical chemistry in 2001 (University of Pisa and Scuola Normale Superiore di Pisa, Italy) and the doctorate in computational chemistry in 2006 (Scuola Normale Superiore di Pisa, Italy) defending a thesis on 'Multi-Grid QM/ MM Approaches in ab initio Molecular Dynamics' supervised by Prof. Dr. Michele Parrinello. From 2006 to 2008, Teo worked as a post-doctoral researcher in the research group of Prof. Dr. Jürg Hutter at the University of Zurich, contributing to the development of the CP2K simulation package. In 2008, Teo joined the IBM Research - Zurich Laboratory (ZRL) as Research Scientist. He is currently Distinguished Research Scientist and manager.
His research interests focus on developing machine learning/artificial intelligence technologies to digitalize chemistry and materials science, with IBM RXN for chemistry being an example of a recent community success. In 2022, the team received the Sandmeyer Award of the Swiss Chemical Society for the important contributions to the field of digital chemistry.


Fueling the Digital Chemistry Revolution with Language and Multimodal Foundation Models


In the last years, natural language processing models have emerged as one of the most effective, scalable approaches for capturing human knowledge and modelling chemical processes in organic chemistry. Its use in machine learning tasks demonstrated high quality and ease of use in problems such as predicting chemical reactions [1-2], retrosynthetic routes [3], digitizing chemical literature [4], predicting detailed experimental procedures [5], designing new fingerprints [6] and yield predictions [7]. In this talk, I'll talk about the impact of language models in chemistry by highlighting the critical role of NLP architectures in implementing the first cloud-based AI-driven autonomous laboratory [8] and in the potential of multimodal foundation models in addressing the data capture problems in experiments performed by human scientists.

  1. IBM Research Europe, Chem. Sci., 2018, 9, 6091-6098

  2. IBM Research Europe, ACS Cent. Sci. 2019, 5, 9, 1572-1583

  3. IBM Research Europe, Chem. Sci., 2020, 11, 3316-3325

  4. IBM Research Europe, Nat. Comm., 2020, 11, 3601

  5. IBM Research Europe, Nat. Comm., 2021, 12, 2573

  6. IBM Research Europe, Nat. Mach. Intel., 2021, 3, 144–152

  7. IBM Research Europe, Mach. Learn.: Sci. Technol., 2021, 2, 015016

  8. https://rxn.res.ibm.com
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