In-silico Polymer Generation using fine-tuned Regression Transformers with Open Reference Data
- Ronaldo Giro
- Hsiang Han Hsu
- et al.
- 2023
- MRS Fall Meeting 2023
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
HyperTaste Lab – A notebook with machine learning pipeline for chemical sensor arrays
- Gianmarco Gabrieli
- Michal Muszynski
- et al.
- 2023
- Eurosensors 2023
Creating labs that learn through automated data management
- Amol Thakkar
- Andrea Giovannini
- et al.
- 2023
- ACS Fall 2023
Harnessing the Power of Language Models to Accelerate Material Design
- 2023
- ACS Fall 2023
Seeing the unseen: leveraging vision models to extract digital fingerprints of complex liquids
- 2023
- ACS Fall 2023
Lab automation, data modeling, and AI agents for accelerated catalyst discovery
- 2023
- ACS Fall 2023
Fueling the Digital Chemistry Revolution with Language Models
- Antonio Cardinale
- Alessandro Castrogiovanni
- et al.
- 2023
- Chimia
Unifying Molecular and Textual Representations via Multi-task Language Modelling
- Dimitrios Christofidellis
- Giorgio Giannone
- et al.
- 2023
- ICML 2023
Artificial intelligence driven design of catalysts and materials for ring opening polymerization using a domain-specific language
- Nathaniel H. Park
- Matteo Manica
- et al.
- 2023
- Nature Communications
A new era of collaborative experimentation enabled by multi-cloud and AI foundation models
- Patrick Ruch
- Alain Vaucher
- et al.
- 2023
- Future Labs Live 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