ArticleChemical science2018
An evolutionary algorithm for the discovery of porous organic cages.
Article in Chemical science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Ion-Permselective Porous Organic Cage Membranes.Angewandte Chemie (International ed. in English) · 2026Review
- Unbiased Structure Prediction of Sophisticated Cage Structures.Angewandte Chemie (International ed. in English) · 2026Article
- 3DOpt: Benchmark for Automated Design of 3D Molecular Structures across the Periodic Table.Journal of chemical information and modeling · 2026Article
- Inverse Design of Metal-Organic Polyhedra through Molecular Fragmentation and Evolutionary Optimisation.Journal of chemical information and modeling · 2026Article
- Exploring organic chemical space for materials discovery using crystal structure prediction-informed evolutionary optimisation.Nature communications · 2025Article
- Comparative analysis of search approaches to discover donor molecules for organic solar cells.Digital discovery · 2025Article
- Beyond symmetric self-assembly and effective molarity: unlocking functional enzyme mimics with robust organic cages.Beilstein journal of organic chemistry · 2025Article
- Design of Recyclable Plastics with Machine Learning and Genetic Algorithm.Journal of chemical information and modeling · 2024Article
- Argentophilic Interactions, Flexibility, and Dynamics of Pyrrole Cages Encapsulating Silver(I) Clusters.The journal of physical chemistry. A · 2024Article
- Programmable synthesis of organic cages with reduced symmetry.Chemical science · 2024Article
- Computational modeling to assist in the discovery of supramolecular materials.Annals of the New York Academy of Sciences · 2022Review
- A data-driven and topological mapping approach for the a priori prediction of stable molecular crystalline hydrates.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Into the Unknown: How Computation Can Help Explore Uncharted Material Space.Journal of the American Chemical Society · 2022Review
- Purely Covalent Molecular Cages and Containers for Guest Encapsulation.Chemical reviews · 2022Review
- Beyond Platonic: How to Build Metal-Organic Polyhedra Capable of Binding Low-Symmetry, Information-Rich Molecular Cargoes.Chemical reviews · 2022Review
- Analogy Powered by Prediction and Structural Invariants: Computationally Led Discovery of a Mesoporous Hydrogen-Bonded Organic Cage Crystal.Journal of the American Chemical Society · 2022Article
- Materials Precursor Score: Modeling Chemists' Intuition for the Synthetic Accessibility of Porous Organic Cage Precursors.Journal of chemical information and modeling · 2021Article
- Integrating Computational and Experimental Workflows for Accelerated Organic Materials Discovery.Advanced materials (Deerfield Beach, Fla.) · 2021Review
- Selective adsorptive separation of cyclohexane over benzene using thienothiophene cages.Chemical science · 2021Article
- Computational discovery of molecular CCommunications chemistry · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The chemical and structural space of possible molecular materials is enormous, as they can, in principle, be built from any combination of organic building blocks. Here we have developed an evolutionary algorithm (EA) that can assist in the efficient exploration of chemical space for molecular materials, helping to guide synthesis to materials with promising applications. We demonstrate the utility of our EA to porous organic cages, predicting both promising targets and identifying the chemical features that emerge as important for a cage to be shape persistent or to adopt a particular cavity size. We identify that shape persistent cages require a low percentage of rotatable bonds in their precursors (<20%) and that the higher topicity building block in particular should use double bonds for rigidity. We can use the EA to explore what size ranges for precursors are required for achieving a given pore size in a cage and show that 16 Å pores, which are absent in the literature, should be synthetically achievable. Our EA implementation is adaptable and easily extendable, not only to target specific properties of porous organic cages, such as optimal encapsulants or molecular separation materials, but also to any easily calculable property of other molecular 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.