A quantitative uncertainty metric controls error in neural network-driven chemical discovery
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Abstract
A predictive approach for driving down machine learning model errors is introduced and demonstrated across discovery for inorganic and organic chemistry.
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Incorporation of OHLDB compounds reduced errors of ML models (i.e., ANNs) on experimental (i.e., CSD) complexes by approximately 25%.
“Incorporation of OHLDB compounds reduced errors of ML models (i.e., ANNs) on experimental (i.e., CSD) complexes by approximately 25% (26,46).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [46]
The Results & discussion section reports that adding 10 selected CSD data points to ANN training reduced mean absolute error on the CSD set from 8.6 to 7.1 kcal mol−1, about 17%, not approximately 25%. Our copy does not identify those added points as OHLDB compounds (searched: OHLDB and 25% reduction). (LLM, conf=0.86)
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- Authors
- Janet, J. P., Duan, C., Yang, T., Nandy, A., & Kulik, H. J.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Kulik, Heather J.
- Venue
- Chemical Science
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2019
- Volume
- 10
- Issue
- 34
- Pages
- 7913-7922
- ISSN
- 2041-6520 (ISSN-L)
- 2041-6539
- PMC
- PMC6764470
- OpenAlex
- W2959623824
- Cited by
- 243
- References
- 87
- Citations by year
- 2026: 43 · 2025: 29 · 2024: 34 · 2023: 23 · 2022: 37 · 2021: 39 · 2020: 29 · 2019: 8 · 2017: 1
- FWCI
- 10.20
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Chemical Science (publishedVersion, cc-by-nc)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Figshare (submittedVersion, cc-by-nc-nd)
- Funders
- Office of Naval Research Global (N00014-18-1-2434)
- Burroughs Wellcome Fund (Career Award at the Scientific Interface)
- American Association for the Advancement of Science (Marion Milligan Mason Award)
- Defense Advanced Research Projects Agency (D18AP00039)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- uncertainty quantification, neural network model, chemical discoveries, prediction error, active learning (machine learning), chemical space exploration, model calibration, inorganic chemistry, organic chemistry
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