Capturing chemical intuition in synthesis of metal-organic frameworks
Open accessAbstract
We report a methodology using machine learning to capture chemical intuition from a set of (partially) failed attempts to synthesize a metal-organic framework. We define chemical intuition as the collection of unwritten guidelines used by synthetic chemists to find the right synthesis conditions. As (partially) failed experiments usually remain unreported, we have reconstructed a typical track of failed experiments in a successful search for finding the optimal synthesis conditions that yields HKUST-1 with the highest surface area reported to date. We illustrate the importance of quantifying this chemical intuition for the synthesis of novel materials.
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In experimental data sets extracted from the literature, there can be positive bias for reporting only successful experiments, leading to challenges in classification tasks.
“For example, in experimental data sets extracted from the literature (14,15,19), there can be positive bias for reporting only successful experiments (16, 107, 108), leading to challenges in classification tasks (17).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [16]
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- Authors
- Moosavi, S. M., Chidambaram, A., Talirz, L., Haranczyk, M., Stylianou, K. C., & Smit, B.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- Corresponding author
- Smit, Berend
- ORCID
- Venue
- Nature Communications
- Published
- 2019-02-01
- Volume
- 10
- Issue
- 1
- Pages
- 539-539
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC6358622
- OpenAlex
- W2910636130
- Cited by
- 257
- References
- 25
- Citations by year
- 2026: 27 · 2025: 42 · 2024: 24 · 2023: 38 · 2022: 37 · 2021: 42 · 2020: 32 · 2019: 15
- FWCI
- 11.37
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Funders
- National Science Foundation
- European Commission (HORIZON2020, 666983)
- Deutsche Forschungsgemeinschaft (SPP1570)
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (166888)
- Ministerio de Economía y Competitividad (RYC-2013-13949)
- National Center of Competence in Research Materials’ Revolution: Computational Design and Discovery of Novel Materials
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Chemistry › Inorganic Chemistry › Metal-Organic Frameworks: Synthesis and Applications
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- metal-organic frameworks, HKUST-1, chemical intuition, machine learning, synthesis condition optimization, failed experiments, surface area, materials discovery
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