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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencescomputer and information sciencesdatabases
- natural sciencesmathematicspure mathematicstopology
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Keywords
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinator
4365 ESCH-SUR-ALZETTE
Luxembourg