Publications
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Physics-informed diffusion models for extrapolating crystal structures beyond known motifs
Andrij Vasylenko, Federico Ottomano, Christopher Collins, Rahul Savani, Matthew Dyer, Matthew RosseinskyarXiv
NA-LR: Noise-Adaptive Low-Rank Parameterisation for Efficient Diffusion Models
Jingyuan Wang, Federico Ottomano, Yingzhen LiRethinking AI EurIPS 2025
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Yoel Zimmerman, Adib Bagzir, Zartashia Azfal, ... Federico Ottomano, Aleyna Beste Ozhan, ...arXiv
Investigating extrapolation and low-data challenges via contrastive learning of chemical compositions
Federico Ottomano, Giovanni De Felice, Rahul Savani, Vladimir Gusev, Vladimir Gusev, Matthew RosseinskyAI4Mat NeurIPS 2023
Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials
Federico Ottomano, John Y. Goulermas, Vladimir Gusev, Rahul Savani, Michael W. Gaultois, Troy D. Manning, Hai Lin, Teresa Partida Manzanera, Emmeline G. Poole, Matthew S. Dyer, John B. Claridge, Jon Alaria, Luke M. Daniels, Su Varma, David Rimmer, Kevin Sanderson, Matthew J. RosseinskyDigital Discovery
Not as simple as we thought: A rigorous examination of data aggregation in materials informatics
Federico Ottomano*, Giovanni De Felice*, Vladimir Gusev, Taylor SparksDigital Discovery
Spectral and ergodic properties of completely positive maps and decoherence
Francesco Fidaleo, Federico Ottomano, Stefano RossiLinear Algebra and its Applications
*Equal contribution.