Accelerating material design with the generative toolkit for scientific discovery
- Matteo Manica
- Jannis Born
- et al.
- 2023
- npj Computational Materials
Bio
Matteo is a Senior Research Scientist in Accelerated Discovery at IBM Research Europe - Zürich. His research interests are AI-driven autonomous labs and multimodal generative models for material science. He received his degree in Mathematical Engineering from Politecnico di Milano in 2013. After getting his MSc, he worked in a startup, Moxoff spa, as a software engineer and analyst for scientific computing. In 2019 he obtained his doctoral degree at the end of a joint Ph.D. program between IBM Research and the Institute of Molecular Systems Biology, ETH Zürich, with a thesis on multimodal learning approaches for precision medicine.
Matteo's full publication record is available on Google Scholar.
Publications
Unifying Molecular and Textual Representations via Multi-task Language Modelling
- Dimitrios Christofidellis
- Giorgio Giannone
- et al.
- 2023
- MRS Spring Meeting 2023
Enzyme Optimization via a Generative Language Modeling-Based Evolutionary Algorithm
- Yves Gaetan Nana Teukam
- Francesca Grisoni
- et al.
- 2023
- MRS Spring Meeting 2023
Growing Strings in a Chemical Reaction Space for Searching Retrosynthesis Pathways
- 2023
- MRS Spring Meeting 2023
Regression Transformer enables concurrent sequence regression and generation for molecular language modelling
- Jannis Born
- Matteo Manica
- 2023
- Nature Machine Intelligence
Chemical representation learning for toxicity prediction
- Jannis Born
- Greta Markert
- et al.
- 2023
- Digital Discovery
Increasing the adoption of machine learning technologies by chemists with graphical and programmatic interfaces
- Alain Vaucher
- Matteo Manica
- et al.
- 2023
- ACS Spring 2023
Enzyme optimization via a generative language modeling-based evolutionary algorithm
- Yves Gaetan Nana Teukam
- Francesca Grisoni
- et al.
- 2023
- ACS Spring 2023
Unifying Molecular and Textual Representations via Multi-task Language Modelling
- Dimitrios Christofidellis
- Giorgio Giannone
- et al.
- 2023
- ACS Spring 2023
Growing strings in a chemical reaction space for searching retrosynthesis pathways
- 2023
- ACS Spring 2023
Patents
- US
- 12124958
- US
- 11942189
- JP
- 7358005
- US
- 11651860
- US
- 11651841
- US
- 11515005
- GB
- 2595994
- US
- 11150926
- US
- 10593422
Projects
Accelerator Technologies
We're developing technological solutions to assist subject matter experts with their scientific workflows by enabling the Human-AI co-creation process.
AI for Scientific Discovery
Creating the AI-enabled lab for a new era of reproducible and collaborative experimentation
Lab that Learns
Leveraging AI foundation models and multi-cloud computing to usher in a new era of reproducible and collaborative experimentation for scientific discovery.
Blog posts
How generative AI models can fuel scientific discovery
ExplainerJohn R Smith and Matteo Manica6 minute read
Top collaborators
TL
Teodoro Laino
Distinguished RSM, Manager
GG
Gianmarco Gabrieli
Pre-Doctoral Researcher
JB
Jannis Born
Research Scientist in AI for Scientific Discovery
FZ
Federico Zipoli
Research Scientist