Digital Fingerprinting of Complex Liquids Using a Reconfigurable Multi-Sensor System with Foundation Models
- 2024
- Advanced Science
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
Predicting polymerization reactions via transfer learning using chemical language models
- Ronaldo Giro
- Brenda S. Ferrari
- et al.
- 2024
- ACS Fall 2024
Catalysts Synthesis Procedures Extraction from Synthesis Paragraphs using Large Language Models
- Daniel Pereira Costa
- Matteo Manica
- et al.
- 2024
- ICCatalysts 2024
Multimodal foundation models for more reproducible scientific experimentation and data capture
- 2024
- Future Labs Live 2024
Growing strings in a chemical reaction space for searching retrosynthesis pathways
- 2024
- npj Computational Materials
Language models can identify enzymatic binding sites in protein sequences
- Yves Gaetan Nana Teukam
- Loic Kwate Dassi
- et al.
- 2024
- Computational And Structural Biotechnology Journal
Using Domain Specific Languages to Enable Artificial Intelligence for Polymer and Catalyst Design
- 2024
- MRS Spring Meeting 2024
Using Foundation Models to Promote Digitization and Reproducibility in Scientific Experimentation
- Amol Thakkar
- Andrea Giovannini
- et al.
- 2023
- NeurIPS 2023
Zero-Shot-BERT-Adapters: a Zero-Shot Pipeline for Unknown Intent Detection
- Daniele Comi
- Dimitrios Christofidellis
- et al.
- 2023
- EMNLP 2023
Enzeptional: enzyme optimization via a generative language modeling-based evolutionary algorithm
- 2023
- MRS Fall Meeting 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