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

Project description

Teaching AI to think like a chemist

Machine learning is revolutionising chemistry, but it still struggles to explain why things work and often fails when faced with unfamiliar molecules. Supported by the Marie Skłodowska-Curie Actions programme, the LocalChemAI project aims to address this by combining cutting-edge AI with a deep dive into quantum chemical topology. By breaking molecules down into local atomic and interatomic parts using a physically rigorous method called atoms-in-molecules fragmentation, the project brings interpretability and transferability to machine predictions. This means not just accurate results, but understandable ones, opening new doors in drug discovery, materials science, and molecular design. LocalChemAI is shaping AI that can think like a chemist, not just calculate like a machine.

Objective

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 expected to benefit fields such as drug design, materials science, and the creation of transferable ML Force Fields (FFs) capable of accurately predicting the behavior of large and complex chemical systems. The project is structured around 4 main work packages (WPs), along with one devoted for data management, career development and dissemination. WP1 will focus on assembling a comprehensive and unbiased database of QCT-AIM properties within the CHONSFCl chemical space, serving as the foundation for model development. WP2 will employ advanced data analysis and pattern recognition techniques to explore relationships between local and global chemical properties, aiding in the refinement of predictive algorithms. WP3 will involve constructing and training predictive algorithms based on local QCT contributions, ensuring their physical accuracy and capability to extrapolate to new chemical contexts. Finally, WP4 will evaluate the applicability of these predictive tools by reconstructing molecular energy landscapes and exploring the inverse design of novel molecules, emphasizing the identification and creation of molecular scaffolds with tailored properties.

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2024-PF-01

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Coordinator

UNIVERSITE DU LUXEMBOURG
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 200 400,00
Address
2 PLACE DE L'UNIVERSITE
4365 ESCH-SUR-ALZETTE
Luxembourg

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Region
Luxembourg Luxembourg Luxembourg
Activity type
Higher or Secondary Education Establishments
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Total cost

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Partners (1)

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