From the 1 of 5 linked papers with an AI index.
1 citations · 1 across the 2 of their papers we have counts for
5 papers
NMIRacle: Multi-modal Generative Molecular Elucidation from IR and NMR Spectra
Federico Ottomano, Yingzhen Li, Alex M. Ganose
Molecular structure elucidation from spectroscopic data is a long-standing challenge in Chemistry, traditionally requiring expert interpretation. We introduce NMIRacle, a two-stage…
Autoregressive latent diffusion for 3D molecule generation
Federico Ottomano, Gaopeng Ren, Yingzhen Li +2
Three-dimensional (3D) molecule generation has been dominated by diffusion models, which achieve strong generation quality but typically require the molecular size to be specified…
Introducing physics-informed generative models for targeting structural novelty in the exploration of chemical space
Andrij Vasylenko, Federico Ottomano, Christopher M. Collins +3
Discovering materials with new structural chemistry is key to achieving transformative functionality. Generative artificial intelligence offers a scalable route to propose candidat…
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Yoel Zimmermann, Adib Bazgir, Zartashia Afzal +141
Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hyb…
Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials
Federico Ottomano, John Y. Goulermas, Vladimir Gusev +14
Machine Learning (ML) has offered innovative perspectives for accelerating the discovery of new functional materials, leveraging the increasing availability of material databases.…