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Local Chemical AI: Achieving Transferability and Interpretability in Machine Learning Models through Quantum Theory of Atoms in Molecules.

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Local Chemical AI: Achieving Transferability and Interpretability in Machine Learning Models through Quantum Theory of Atoms in Molecules. 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. By integrating this precise partitioning with cutting-edge ML techniques, I seek to develop AI systems that not only predict molecular properties with high accuracy but also provide insights into underlying physical principles, resulting in more interpretable and generalizable predictions. The anticipated advancements are expecte

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recordType

award

status

SIGNED

region

EU

value

200400

unit

EUR