BetaCitation Atlas

RESEARCH PAPER · Journal of Chemical Information and Modeling · 2021

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

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

Read original paper ↗

See where this research leads.

26 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
Filter Citing Papers
Press Enter or comma to add · × to remove · searches titles and available abstracts
Journals
All journals
Most represented first · select one or more
Explore Terms In These PapersHide SuggestionsShow Suggestions

Original and current center papers stay visible as anchors.
Numbers show citations received
42 direct citing works

Materials Precursor Score: Modeling Chemists’ Intuition for the Synthetic Accessibility of Porous Organic Cage PrecursorsOriginalBayesian optimization of nanoporous materials112Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery98Coarse-grained modelling to predict the packing of porous organic cages12Unlocking the computational design of metal–organic cages66Rigidity and Flexibility in Rotaxanes and Their Relatives; On Being Stubborn and Easy-Going31Roles and opportunities for machine learning in organic molecular crystal structure prediction and its applications18Computational modeling to assist in the discovery of supramolecular materials20Experimental and Computational Approaches to Sulfonated Poly(arylene ether sulfone) Synthesis Using Different Halogen Atoms at the Reactive Site0Porous Molecular Materials1Unveiling Hidden Zeolitic Imidazolate Frameworks Guided by Intuition‐Based Geometrical Factors15Learning chemical intuition from humans in the loop4Learning chemical intuition from humans in the loop1Functional Material Systems Enabled by Automated Data Extraction and Machine Learning33Extracting medicinal chemistry intuition via preference machine learning36Design and assembly of porous organic cages51Investigating the reliability and interpretability of machine learning frameworks for chemical retrosynthesis15Streamlining the automated discovery of porous organic cages22Supramolecular and molecular capsules, cages and containers118E_min: A First-Principles Thermochemical Descriptor for Predicting Molecular Synthesizability1Emin: A First-Principles Thermochemical Descriptor for Predicting Molecular Synthesizability5Uncovering Zeolitic Imidazolate Frameworks through Digitization of Chemical Insights1Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry51Synthetic accessibility scoring and its application to the high-throughput design of energetic molecules7Data-Driven Search Algorithm for Discovery of Synthesizable Zeolitic Imidazolate Frameworks8Accelerating the Transformation of Highly Energetic Molecules from “on Paper” to Synthesizable Targets: Synthetic Accessibility Assessment with Scoring Models0Biochar-based carbon nanomaterials: precursor variability and structure-property heterogeneity for energy-environmental applications0

Arrows point to the paper being cited. Selecting a paper shows its incoming citations and its known path back to the original paper.

Drag to pan · Use + / − to zoom · Escape to close43 nodes / 53 citation links
Browse Matching Papers

Papers that cite this work

26 direct citing papers saved.

  1. Bayesian optimization of nanoporous materials

    Molecular Systems Design & Engineering · 2021-01-01

    Aryan Deshwal, Cory M. Simon, Janardhan Rao Doppa

    Read original paper ↗ ·

  2. Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery

    Current Opinion in Chemical Engineering · 2021-12-17

    Aditya Nandy, Chenru Duan, Heather J. Kulik

    Read original paper ↗ ·

  3. Coarse-grained modelling to predict the packing of porous organic cages

    Chemical Science · 2022-01-01

    Emma Helen Wolpert, Kim E. Jelfs

    Read original paper ↗ ·

  4. Unlocking the computational design of metal–organic cages

    Chemical Communications · 2022-01-01

    Andrew Tarzia, Kim E. Jelfs

    Read original paper ↗ ·

  5. 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

    Read original paper ↗ ·

  6. Roles and opportunities for machine learning in organic molecular crystal structure prediction and its applications

    MRS Bulletin · 2022-10-01

    Rebecca J. Clements, Joshua Dickman, Jay Johal, Jennie Martin, Joseph Glover, Graeme Matthew Day

    Read original paper ↗ ·

  7. Computational modeling to assist in the discovery of supramolecular materials

    Annals of the New York Academy of Sciences · 2022-10-17

    Kim E. Jelfs

    Read original paper ↗ ·

  8. Experimental and Computational Approaches to Sulfonated Poly(arylene ether sulfone) Synthesis Using Different Halogen Atoms at the Reactive Site

    Membranes · 2022-12-19

    Seol A Jang, Jung-Eun Cha, Seung Jae Moon, Justin Georg Albers, Min Ho Seo, Young Woo Choi, Jong Hak Kim

    Read original paper ↗ ·

  9. Porous Molecular Materials

    Unknown journal · 2023-01-24

    Steven Bennett, Kim E. Jelfs

    Read original paper ↗ ·

  10. Unveiling Hidden Zeolitic Imidazolate Frameworks Guided by Intuition‐Based Geometrical Factors

    Small · 2023-02-09

    Soochan Lee, Dongsik Nam, David ChangMo Yang, Wonyoung Choe

    Read original paper ↗ ·

  11. Learning chemical intuition from humans in the loop

    ChemRxiv · 2023-02-24

    Oh-Hyeon Choung, Riccardo Vianello, Marwin Segler, Nikolaus Stiefl, José Jiménez-Luna

    Read original paper ↗ ·

  12. Learning chemical intuition from humans in the loop

    ChemRxiv · 2023-03-16

    Oh-Hyeon Choung, Riccardo Vianello, Marwin Segler, Nikolaus Stiefl, José Jiménez-Luna

    Read original paper ↗ ·

  13. Functional Material Systems Enabled by Automated Data Extraction and Machine Learning

    Advanced Functional Materials · 2023-06-05

    Payam Kalhor, Nicole Jung, Stefan Bräse, Christof Wöll, Manuel M. Tsotsalas, Pascal Friederich

    Read original paper ↗ ·

  14. Extracting medicinal chemistry intuition via preference machine learning

    Nature Communications · 2023-10-31

    Oh-Hyeon Choung, Riccardo Vianello, Marwin Segler, Nikolaus Stiefl, José Jiménez-Luna

    Read original paper ↗ ·

  15. Design and assembly of porous organic cages

    Chemical Communications · 2024-01-01

    Zezhao Xu, Yangzhi Ye, Yilan Liu, Huiyu Liu, Shan Hao Jiang

    Read original paper ↗ ·

  16. Investigating the reliability and interpretability of machine learning frameworks for chemical retrosynthesis

    Digital Discovery · 2024-01-01

    Friedrich Hastedt, Rowan M. Bailey, Klaus Hellgardt, Sophia N. Yaliraki, Ehecatl Antonio del Rio‐Chanona, Dongda Zhang

    Read original paper ↗ ·

  17. Streamlining the automated discovery of porous organic cages

    Chemical Science · 2024-01-01

    Annabel R. Basford, Steven K. Bennett, Muye Xiao, Lukas Turcani, Jasmine Allen, Kim E. Jelfs, Rebecca L. Greenaway

    Read original paper ↗ ·

  18. Supramolecular and molecular capsules, cages and containers

    Chemical Society Reviews · 2024-01-01

    Cameron J. T. Cox, Jessica Hale, Paulina Molinska, James E. M. Lewis

    Read original paper ↗ ·

  19. E_min: A First-Principles Thermochemical Descriptor for Predicting Molecular Synthesizability

    ChemRxiv · 2024-01-30

    Andrew Lee, Sarah N. Elliott, Hassan Harb, Logan T. Ward, Ian T Foster, Larry A. Curtiss, Rajeev S. Assary

    Read original paper ↗ ·

  20. Emin: A First-Principles Thermochemical Descriptor for Predicting Molecular Synthesizability

    Journal of Chemical Information and Modeling · 2024-02-15

    Andrew S. Lee, Sarah N. Elliott, Hassan Harb, Logan T. Ward, Ian T Foster, Larry A. Curtiss, Rajeev S. Assary

    Read original paper ↗ ·

  21. Uncovering Zeolitic Imidazolate Frameworks through Digitization of Chemical Insights

    ChemRxiv · 2024-04-12

    Soochan Lee, Hyein Jeong, Sungyeop Jung, Yeong-Jin Kim, Eunchan Cho, Joohan Nam, David ChangMo Yang, Hyunchul Oh, Wonyoung Choe

    Read original paper ↗ ·

  22. Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry

    Chemical Society Reviews · 2025-01-01

    Austin M. Mroz, Annabel R. Basford, Friedrich Hastedt, Isuru Shavindra Jayasekera, Irea Mosquera‐Lois, Ruby Sedgwick, Pedro J. Ballester, Joshua D. Bocarsly, Ehecatl Antonio del Rio‐Chanona, Matthew L. Evans, Jarvist Moore Frost, Alex M. Ganose, Rebecca L. Greenaway, King Kuok Hii, Yingzhen Li, Ruth Misener, Aron Walsh, Dandan Zhang, Kim E. Jelfs

    Read original paper ↗ ·

  23. Synthetic accessibility scoring and its application to the high-throughput design of energetic molecules

    Journal of Materials Chemistry A · 2025-01-01

    Yining Zhang, Zhixiang Zhang, Linyuan Wen, Yingzhe Liu

    Read original paper ↗ ·

  24. Data-Driven Search Algorithm for Discovery of Synthesizable Zeolitic Imidazolate Frameworks

    JACS Au · 2025-03-07

    Soochan Lee, Hyein Jeong, Sungyeop Jung, Yeong-Jin Kim, Eunchan Cho, Joohan Nam, David ChangMo Yang, Dong Yun Shin, Jung-Hoon Lee, Hyunchul Oh, Wonyoung Choe

    Read original paper ↗ ·

  25. Accelerating the Transformation of Highly Energetic Molecules from “on Paper” to Synthesizable Targets: Synthetic Accessibility Assessment with Scoring Models

    The Journal of Physical Chemistry A · 2026-03-18

    Y. L. Zhang, Xiaokai He, Chao Chen, Linyuan Wen, Tao Yu, Guojin Li, Xiaoyan Wang, Zhixiang Zhang, Yingzhe Liu

    Read original paper ↗ ·

  26. Biochar-based carbon nanomaterials: precursor variability and structure-property heterogeneity for energy-environmental applications

    Biochar · 2026-10-13

    Noor Ul Huda Altaf, Muhammad Ayyaz, Chanatip Samart, Minyi He, Faizan Ahmad, Yaning Zhang

    Read original paper ↗ ·
Citation Atlas · A map of research, not a ranking.