Improved Chemical Prediction from Scarce Data Sets via Latent Space Enrichment
Abstract
Modern machine learning provides promising methods for accelerating the discovery and characterization of novel chemical species. However, in many areas experimental data remain costly and scarce, and computational models are unavailable for targeted figures of merit. Here we report a promising pathway to address this challenge by using chemical latent space enrichment, whereby disparate data sources are combined in joint prediction tasks to enable improved prediction in data-scarce applications. The approach is demonstrated for p K a prediction of moderately sized molecular species using a combination of experimentally available p K a data and density functional theory-based characterizations of the (de)protonation free energy. A novel autoencoder framework is used to create a continuous chemical latent space that is then used in single and joint training tasks for property prediction. By combining these two data sets in a jointly trained autoencoder framework, we observe mutual improvement in property prediction tasks in the scarce data limit. We also demonstrate an enrichment mechanism that is unique to latent space training, whereby training on excess computational data can mitigate the prediction losses associated with scarce experimental data and advantageously organize the latent space. These results demonstrate that disparate chemical data sources can be advantageously combined in an autoencoder framework with potential general application to data-scarce chemical learning tasks.
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These techniques can be applied to the latent space of models just as readily as the feature space, for example, to highlight regions of promise for enrichment or to reveal governing design principles.
“These techniques can be applied to the latent space of models just as readily as the feature space, for example, to highlight regions of promise for enrichment (37,65) or to reveal governing design principles (64).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [65]
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- Authors
- Iovanac, N. C., & Savoie, B. M.
- Affiliations
- Purdue University West Lafayette (US)
- Corresponding author
- Savoie, Brett M.
- ORCID
- Savoie, Brett M.
- Venue
- The Journal of Physical Chemistry A
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-04-29
- Volume
- 123
- Issue
- 19
- Pages
- 4295-4302
- ISSN
- 1089-5639 (ISSN-L)
- 1520-5215
- OpenAlex
- W2943486344
- Cited by
- 41
- References
- 60
- Citations by year
- 2025: 1 · 2024: 5 · 2023: 5 · 2022: 7 · 2021: 8 · 2020: 8 · 2019: 7
- FWCI
- 2.71
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- The Journal of Physical Chemistry A (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Division of Graduate Education (DGE-1333468)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- pKa prediction, autoencoder, density functional theory, chemical property prediction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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