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RESEARCH PAPER · Royal Society of Chemistry eBooks · 2020

Artificial Intelligence Applied to the Prediction of Organic Materials

Steven Bennett, Andrew Tarzia, Martijn A. Zwijnenburg, Kim E. Jelfs

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12 direct citing papers in this saved network. Explore the next layer with second-order citations.

Open in your workspace →Saved 2026-10-14 · complete coverage
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42 direct citing works

Artificial Intelligence Applied to the Prediction of Organic MaterialsOriginalCan we predict materials that can be synthesised?73A Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials0Materials Precursor Score: Modeling Chemists’ Intuition for the Synthetic Accessibility of Porous Organic Cage Precursors27Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers61Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials25Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials0Rigidity and Flexibility in Rotaxanes and Their Relatives; On Being Stubborn and Easy-Going31Inverse molecular design from first principles: Tailoring organic chromophore spectra for optoelectronic applications24Photocatalytic molecular containers enable unique reactivity modes in confinement2Porous Molecular Materials1On the use of Machine Learning modeling tools in chemical process and product engineering0The PPP model – a minimum viable parametrisation of conjugated chemistry for modern computing applications3

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12 direct citing papers saved.

  1. Can we predict materials that can be synthesised?

    Chemical Science · 2020-12-10

    Filip Szczypiński, Steven Bennett, Kim E. Jelfs

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  2. A Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

    ChemRxiv · 2021-03-16

    Árni Sturluson, Ali Raza, Grant D. McConachie, Daniel W. Siderius, Xiaoli Z. Fern, Cory M. Simon

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  3. Materials Precursor Score: Modeling Chemists’ Intuition for the Synthetic Accessibility of Porous Organic Cage Precursors

    Journal of Chemical Information and Modeling · 2021-08-13

    Steven Bennett, Filip Szczypiński, Lukas Turcani, Michael E. Briggs, Rebecca L. Greenaway, Kim E. Jelfs

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  4. Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers

    Processes · 2021-08-20

    Cindy Trinh, Dimitrios Meimaroglou, Sandrine Hoppe

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  5. Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

    Chemistry of Materials · 2021-09-07

    Árni Sturluson, Ali Raza, Grant D. McConachie, Daniel W. Siderius, Xiaoli Z. Fern, Cory M. Simon

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  6. Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

    Unknown journal · 2021-10-26

    Árni Sturluson, Ali Raza, Grant D. McConachie, Daniel W. Siderius, Xiaoli Z. Fern, Cory M. Simon

    Read original paper ↗ ·

  7. Rigidity and Flexibility in Rotaxanes and Their Relatives; On Being Stubborn and Easy-Going

    Frontiers in Chemistry · 2022-04-07

    Rachel E. Fadler, Amar H. Flood

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  8. Inverse molecular design from first principles: Tailoring organic chromophore spectra for optoelectronic applications

    The Journal of Chemical Physics · 2022-04-22

    James D. Green, Eric G. Fuemmeler, Timothy J. H. Hele

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  9. Photocatalytic molecular containers enable unique reactivity modes in confinement

    Tetrahedron Letters · 2022-07-29

    Senuri G. Jayawardana, Ella C. Madura, Víctor García‐López

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  10. Porous Molecular Materials

    Unknown journal · 2023-01-24

    Steven Bennett, Kim E. Jelfs

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  11. On the use of Machine Learning modeling tools in chemical process and product engineering

    Unknown journal · 2023-11-24

    Cindy Trinh

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  12. The PPP model – a minimum viable parametrisation of conjugated chemistry for modern computing applications

    Digital Discovery · 2026-01-01

    Marcel David Fabian, Nina Glaser, Gemma C. Solomon

    Read original paper ↗ ·
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