Active Deep Kernel Learning of Molecular Properties: Realizing Dynamic Structural Embeddings
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916845764739072 |
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| author | Ghosh, Ayana Ziatdinov, Maxim Kalinin, Sergei V. |
| author_facet | Ghosh, Ayana Ziatdinov, Maxim Kalinin, Sergei V. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_01234 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Active Deep Kernel Learning of Molecular Properties: Realizing Dynamic Structural Embeddings Ghosh, Ayana Ziatdinov, Maxim Kalinin, Sergei V. Machine Learning Chemical Physics Computational Physics Data Analysis, Statistics and Probability 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. |
| title | Active Deep Kernel Learning of Molecular Properties: Realizing Dynamic Structural Embeddings |
| topic | Machine Learning Chemical Physics Computational Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2403.01234 |