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

2024/03/02 by Ayana Ghosh, Ghosh, Ayana, Sergei V. Kalinin +2 · 1 citation
Computer Science · Materials Science · #Chemical Physics (physics.chem-ph) #Computational Drug Discovery Methods #Computational Physics (physics.comp-ph) #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Statistics and Probability (physics.data-an)

paper · pdf · doi:10.48550/arxiv.2403.01234

openalex publication_date 2024/03/02 · openalex created_date 2024/03/06 · openalex updated_date 2026/07/31

Abstract

As vast databases of chemical identities become increasingly available, the challenge shifts to how we effectively explore and leverage these resources to study molecular properties. This paper presents an active learning approach for molecular discovery using Deep Kernel Learning (DKL), demonstrated on the QM9 dataset. DKL links structural embeddings directly to properties, creating organized latent spaces that prioritize relevant property information. By iteratively recalculating embedding vectors in alignment with target properties, DKL uncovers concentrated maxima representing key molecular properties and reveals unexplored regions with potential for innovation. This approach underscores DKL's potential in advancing molecular research and discovery.

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