The Alegre Group

Chemical Machine Learning · Cheminformatics

Research

Our research group focuses on machine learning to predict chemical outcomes and properties. We work closely with experimentalists who aim to accelerate chemical discovery through data-driven strategies, moving beyond traditional and inefficient trial-and-error approaches. In addition, we use DFT mechanistic studies to complement the projects.

Cheminformatics is also part of our research scope, including automated workflows for quantum mechanical protocols, such as the post-processing of calculations, conformer generation, and data analysis. Also, we automate machine learning workflows to predict chemical results using structural features and data obtained from quantum mechanical calculations.

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Chemical Machine Learning

Machine learning to study chemical outcomes
and reaction mechanisms

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Cheminformatics Toolkits

Cheminformatic tools for machine learning and
automation of computational chemistry

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Computational Mechanisms

Quantum chemistry and statistical machine
learning to study reaction mechanisms