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

Ayana Ghosh; Maxim Ziatdinov; Sergei V. Kalinin

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

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arxiv 2403.01234 v2 pith:GG6R2QYT submitted 2024-03-02 cs.LG physics.chem-phphysics.comp-phphysics.data-an

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

Ayana Ghosh , Maxim Ziatdinov , Sergei V. Kalinin This is my paper

classification cs.LG physics.chem-phphysics.comp-phphysics.data-an

keywords molecularpropertieslearningactiveapproachdeepdiscoveryembeddings

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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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