Active Deep Kernel Learning of Molecular Functionalities: Realizing Dynamic Structural Embeddings

arXiv:2403.01234v1 Announce Type: new
Abstract: Exploring molecular spaces is crucial for advancing our understanding of chemical properties and reactions, leading to groundbreaking innovations in materials science, medicine, and energy. This paper explores an approach for active learning in molecular discovery using Deep Kernel Learning (DKL), a novel approach surpassing the limits of classical Variational Autoencoders (VAEs). Employing the QM9 dataset, we contrast DKL with traditional VAEs, which analyze molecular structures based on similarity, revealing limitations due to sparse regularities in …

abstract active learning arxiv cs.lg discovery dynamic embeddings energy groundbreaking innovations kernel materials materials science medicine novel paper physics.chem-ph physics.comp-ph physics.data-an science spaces type understanding