Chemistry Publications
These are my published research outputs. I'll be happy to discuss these further via email.
My work centers on predictive inverse design and machine learning (ML) for chemistry under realistic constraints. I specialize in inverse/generative molecular design, cheminformatics, and transformer models for SMILES/RDKit workflows and reaction-data analytics. Among others, I developed a supervised ML model for predicting vanadium-catalyzed epoxidation yields, an explainable model for predicting solvation Gibbs energy, and an inverse, de novo generative model for designing vanadyl-based epoxidation catalyst ligands across a large chemical space.
Data Driven Chemistry
♦ AutoVap: An Interactive Machine Learning Tool for Predicting the Standard Enthalpy of Vaporization • Chemie Ingenieur Technik (2026) Publisher Site PDF
♦ A Hybrid Machine Learning and Quantum Mechanical Strategy for Predicting Radical Scavenging Potential (with Davide Zeppilli, M. Natália D. S. Cordeiro and Laura Orian) • AI Chemistry (2026) Publisher Site PDF
♦ Inverse ligand design: a generative data-driven model for optimizing vanadyl-based epoxidation catalysts (with Filipe Teixeira, M. Natália D. S. Cordeiro and Tomoyuki Miyao) • Journal of Catalysis (2026) Publisher Site PDF
♦Optimizing Vanadium-Catalyzed Epoxidation Reactions: Machine-Learning-Driven Yield Predictions and Data Augmentation (with Filipe Teixeira and M. Natália D. S. Cordeiro) • Journal of Chemical Information and Modeling (2025) Publisher Site PDF
♦ Optimising Materials Properties with Minimal Data: Lessons from Vanadium Catalyst Modelling (book chapter with Filipe Teixeira and M. Natália D. S. Cordeiro) • Materials Informatics I: Methods - Springer Nature Book Series (2025) Publisher Site PDF
♦ Data-driven, explainable machine learning model for predicting volatile organic compounds’ standard vaporization enthalpy (with Filipe Teixeira and M. Natália D. S. Cordeiro) • Chemosphere (2024) Publisher Site PDF
♦ Navigating Epoxidation Complexity: Building a Data Science Toolbox to Design Vanadium Catalysts (with Filipe Teixeira and M. Natália D. S. Cordeiro) • New Journal of Chemistry (2024) Publisher Site PDF
♦ Explainable Supervised Machine Learning Model To Predict Solvation Gibbs Energy (with Filipe Teixeira and M. Natália D. S. Cordeiro) • Journal of Chemical Information and Modeling (2023) Publisher Site PDF
♦ Systematic Development of Vanadium Catalysts for Sustainable Epoxidation of Small Alkenes and Allylic Alcohols (with Filipe Teixeira and M. Natália D. S. Cordeiro) • International Journal of Molecular Sciences (2023) Publisher Site PDF
♦ Current Outlooks on Machine Learning Methods for Development of Industrial Homogeneous Catalytic Systems • Current Organocatalysis (2022) Publisher Site PDF
