CORDIS Project
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This project leverages Quantum Chemical Topology to improve the transferability and interpretability of machine learning models in chemistry. By decomposing molecular properties, it aims to create predictive algorithms that are both accurate and insightful for various applications.
Despite its remarkable accuracy, Machine Learning (ML) in chemistry faces significant challenges in transferability and interpretability, which limits its effectiveness in broader chemical contexts beyond the training data. I aim to overcome these limitations by leveraging Quantum Chemical Topology (QCT) for a physically rigorous atoms-in-molecules (AIM) fragmentation.
This approach allows for the unbiased decomposition of molecular properties into local (atomic and interatomic) contributions. B…
UNIVERSITE DU LUXEMBOURG
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Canada, Toronto
Type: University / higher education
Activity type: Higher or Secondary Education Establishments
SME: No
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