ArticleNature communications2019
Capturing chemical intuition in synthesis of metal-organic frameworks.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
What it found
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Who cites it
51 citing papers in PubMed.
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- Carboxylate-Ligand-Based Coordination Polymers and Metal Complexes: From Structural Diversity and Supramolecular Descriptors to Function-Oriented Design.Molecules (Basel, Switzerland) · 2026Review
- Biomacromolecule-MOF Composites for Intracellular Delivery: From Empirical Construction to Rational Design and AI-Assisted Screening.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Data Management and Analysis of Metal-Organic Framework Synthesis Using Data Models.Journal of chemical information and modeling · 2026Article
- Synthetic Aspects and Characterization Needs in MOF Chemistry - from Discovery to Applications.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Single Crystals of Vanadium Oxides as a Lens for Understanding Structural and Electronic Phase Transformations, Ion Transport, Chemo-Mechanical Coupling, and Electrothermal Neuronal Emulation.Chemical reviews · 2025Review
- Discovery of highly fluorescent covalent organic frameworks through AI-assisted iterative experiment-learning cycles.Nature chemistry · 2025Article
- Connecting metal-organic framework synthesis to applications using multimodal machine learning.Nature communications · 2025Article
- Data-Driven Search Algorithm for Discovery of Synthesizable Zeolitic Imidazolate Frameworks.JACS Au · 2025Article
- Assessment of fine-tuned large language models for real-world chemistry and material science applications.Chemical science · 2025Article
- From Data to Discovery: Recent Trends of Machine Learning in Metal-Organic Frameworks.JACS Au · 2024Review
- Self-Driving Laboratories for Chemistry and Materials Science.Chemical reviews · 2024Review
- Unified Roadmap for ZIF-8 Nucleation and Growth: Machine Learning Analysis of Synthetic Variables and Their Impact on Particle Size and Morphology.Chemistry of materials : a publication of the American Chemical Society · 2024Article
- Illuminating 'the ugly side of science': fresh incentives for reporting negative results.Nature · 2024Article
- Accelerated chemical science with AI.Digital discovery · 2024Review
- Accelerated Discovery of Metal-Organic Frameworks for COIndustrial & engineering chemistry research · 2024Review
- Challenges in modelling dynamic processes in realistic nanostructured materials at operating conditions.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Review
- Performance evaluation of E-nose and E-tongue combined with machine learning for qualitative and quantitative assessment of bear bile powder.Analytical and bioanalytical chemistry · 2023Article
- DigiMOF: A Database of Metal-Organic Framework Synthesis Information Generated via Text Mining.Chemistry of materials : a publication of the American Chemical Society · 2023Article
- Quid Pro Flow.Journal of the American Chemical Society · 2023Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.