BetaCitation Atlas

RESEARCH PAPER · Journal of Chemical Information and Modeling · 2020

What Does the Machine Learn? Knowledge Representations of Chemical Reactivity

Joshua A. Kammeraad, Jack Goetz, Eric A. Walker, Ambuj Tewari, Paul M. Zimmerman

Read original paper ↗

See where this research leads.

30 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

What Does the Machine Learn? Knowledge Representations of Chemical ReactivityOriginalGraph Theory Model of Dry Reforming of Methane Using Rh(111)23Thousands of reactants and transition states for competing E2 and S N 2 reactions50Characterizing Adsorption Sites on Ag/SSZ-13 Zeolites: Experimental Observations and Bayesian Inference16CheKiPEUQ Intro 2: Harnessing Uncertainties from Data Sets, Bayesian Design of Experiments in Chemical Kinetics**29Modeling the Conformational Preference, Spectral Analysis and Other Quantum Mechanical Studies on Three Bioactive Aminobenzoate Derivatives and Their SERS Active Graphene Complexes15Artificial intelligence and automation in computer aided synthesis planning88A review on machine learning algorithms for the ionic liquid chemical space196DeepReac+: deep active learning for quantitative modeling of organic chemical reactions52Cross-validation strategies in QSPR modelling of chemical reactions21Organic reactivity from mechanism to machine learning175PSI4EDUCATION: Free and Open-Source Programing Activities for Chemical Education with Free and Open-Source Software6Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats323Artificial intelligence in reaction prediction and chemical synthesis76Computational modeling of green hydrogen generation from photocatalytic H2S splitting: Overview and perspectives40Predicting reaction conditions from limited data through active transfer learning78Bayesian statistics in catalysis: a perspective11Successes and challenges in using machine-learned activation energies in kinetic simulations34Explainable Solvation Free Energy Prediction Combining Graph Neural Networks with Chemical Intuition43Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights24Improving Accuracy and Transferability of Machine Learning Chemical Activation Energies by Adding Electronic Structure Information10Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit39Application of machine learning in chemical engineering: outlook and perspectives31Immunoinformatics investigation on pathogenic Escherichia coli proteome to develop an epitope-based peptide vaccine candidate9Recommending reaction conditions with label ranking6Advancements in Machine Learning Predicting Activation and Gibbs Free Energies in Chemical Reactions2Local reaction condition optimization via machine learning1Construction of a deep - learning - based rehabilitation prediction model for lower-limb motor dysfunction after stroke using synchronous EEG-EMG and fMRI5Dyna-Q Learning Algorithm for Temperature Control in Exothermic Process0A machine learning framework for long-distance significant wave height prediction in the Yellow Sea with multi-station fusion and interpretability analysis0The Role of Knowledge Representation & Reasoning in Deciphering Chemical Complexity0

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

30 direct citing papers saved.

  1. Graph Theory Model of Dry Reforming of Methane Using Rh(111)

    The Journal of Physical Chemistry Letters · 2020-05-27

    Eric A. Walker, Mohammad Moein Mohammadi, Mark T. Swihart

    Read original paper ↗ ·

  2. Thousands of reactants and transition states for competing E2 and S N 2 reactions

    Machine Learning Science and Technology · 2020-07-21

    Guido Falk von Rudorff, Stefan Heinen, Marco Bragato, O. Anatole von Lilienfeld

    Read original paper ↗ ·

  3. Characterizing Adsorption Sites on Ag/SSZ-13 Zeolites: Experimental Observations and Bayesian Inference

    The Journal of Physical Chemistry C · 2020-08-05

    Caitlin Horvatits, Jungkuk Lee, Eleni A. Kyriakidou, Eric A. Walker

    Read original paper ↗ ·

  4. CheKiPEUQ Intro 2: Harnessing Uncertainties from Data Sets, Bayesian Design of Experiments in Chemical Kinetics**

    ChemCatChem · 2020-08-26

    Eric A. Walker, Kishore Ravisankar, Aditya Savara

    Read original paper ↗ ·

  5. Modeling the Conformational Preference, Spectral Analysis and Other Quantum Mechanical Studies on Three Bioactive Aminobenzoate Derivatives and Their SERS Active Graphene Complexes

    Polycyclic aromatic compounds · 2020-10-01

    Jamelah Saleh Al-Otaibi, Aljawhara H. Almuqrin, Yohannan Sheena Mary, Y. Shyma Mary, Renjith Thomas

    Read original paper ↗ ·

  6. Artificial intelligence and automation in computer aided synthesis planning

    Reaction Chemistry & Engineering · 2020-11-05

    Amol Thakkar, Simon Johansson, Kjell Jorner, David Buttar, Jean‐Louis Reymond, Ola Engkvist

    Read original paper ↗ ·

  7. A review on machine learning algorithms for the ionic liquid chemical space

    Chemical Science · 2021-01-01

    Spyridon Koutsoukos, Frederik Philippi, Francisco Malaret, Tom Welton

    Read original paper ↗ ·

  8. DeepReac+: deep active learning for quantitative modeling of organic chemical reactions

    Chemical Science · 2021-01-01

    Yukang Gong, Dongyu Xue, Guohui Chuai, Jing Yu, Qi Liu

    Read original paper ↗ ·

  9. Cross-validation strategies in QSPR modelling of chemical reactions

    SAR and QSAR in environmental research · 2021-02-19

    Assima Rakhimbekova, Tagir N. Akhmetshin, Guzel Minibaeva, Нугманов Рамиль Ирекович, Timur Rustemovich Gimadiev, Timur Madzhidov, Igor I. Baskin, Alexandre A. Varnek

    Read original paper ↗ ·

  10. Organic reactivity from mechanism to machine learning

    Nature Reviews Chemistry · 2021-03-16

    Kjell Jorner, Anna Tomberg, Christoph Alexander Bauer, Christian Sköld, Per‐Ola Norrby

    Read original paper ↗ ·

  11. PSI4EDUCATION: Free and Open-Source Programing Activities for Chemical Education with Free and Open-Source Software

    ACS symposium series · 2021-07-27

    D. Brandon Magers, Victor H. Chavez, Benjamin G. Peyton, Dominic A. Sirianni, Ryan C. Fortenberry, Ashley Ringer McDonald

    Read original paper ↗ ·

  12. Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats

    Engineering · 2021-07-29

    Maarten R. Dobbelaere, Pieter P. Plehiers, Ruben Van de Vijver, Christian Victor Stevens, Kevin M. Van Geem

    Read original paper ↗ ·

  13. Artificial intelligence in reaction prediction and chemical synthesis

    Current Opinion in Chemical Engineering · 2021-10-22

    Venkat Venkatasubramanian, Vipul Mann

    Read original paper ↗ ·

  14. Computational modeling of green hydrogen generation from photocatalytic H2S splitting: Overview and perspectives

    Journal of Photochemistry and Photobiology C Photochemistry Reviews · 2021-11-09

    Yuting Li, Daniel Bahamón, Mutasem Omar Sinnokrot, Khalid A. Al-Ali, Giovanni Palmisano, Lourdes F. Vega

    Read original paper ↗ ·

  15. Predicting reaction conditions from limited data through active transfer learning

    Chemical Science · 2022-01-01

    Eunjae Shim, Joshua A. Kammeraad, Ziping Xu, Ambuj Tewari, Tim Cernak, Paul M. Zimmerman

    Read original paper ↗ ·

  16. Bayesian statistics in catalysis: a perspective

    Current Opinion in Chemical Engineering · 2022-04-29

    Eric A. Walker

    Read original paper ↗ ·

  17. Successes and challenges in using machine-learned activation energies in kinetic simulations

    The Journal of Chemical Physics · 2022-06-14

    Idil Ismail, Christopher Robertson, Scott Habershon

    Read original paper ↗ ·

  18. Explainable Solvation Free Energy Prediction Combining Graph Neural Networks with Chemical Intuition

    Journal of Chemical Information and Modeling · 2022-11-01

    Kaycee Low, Michelle L. Coote, Ekaterina I. Izgorodina

    Read original paper ↗ ·

  19. Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights

    The Journal of Chemical Physics · 2023-01-30

    Yuxinxin Chen, Yanchi Ou, Peikun Zheng, Yaohuang Huang, Fuchun Ge, Pavlo O. Dral

    Read original paper ↗ ·

  20. Improving Accuracy and Transferability of Machine Learning Chemical Activation Energies by Adding Electronic Structure Information

    Journal of Chemical Information and Modeling · 2023-03-03

    Esteban A. Marques, Stefan De Gendt, Geoffrey Pourtois, Michiel J. van Setten

    Read original paper ↗ ·

  21. Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit

    Journal of Chemical Information and Modeling · 2023-06-14

    Eunjae Shim, Ambuj Tewari, Tim Cernak, Paul M. Zimmerman

    Read original paper ↗ ·

  22. Application of machine learning in chemical engineering: outlook and perspectives

    IAES International Journal of Artificial Intelligence · 2023-12-25

    Ashraf Al Sharah, Hamza Abu Owida, Feras Alnaimat, Suhaila Farhan Ahmad Abuowaida

    Read original paper ↗ ·

  23. Immunoinformatics investigation on pathogenic Escherichia coli proteome to develop an epitope-based peptide vaccine candidate

    Molecular Diversity · 2024-11-08

    Soham Chowdhury, Pinkan Sadhukhan, Nibedita Mahata

    Read original paper ↗ ·

  24. Recommending reaction conditions with label ranking

    Chemical Science · 2025-01-01

    Eunjae Shim, Ambuj Tewari, Tim Cernak, Paul M. Zimmerman

    Read original paper ↗ ·

  25. Advancements in Machine Learning Predicting Activation and Gibbs Free Energies in Chemical Reactions

    International Journal of Quantum Chemistry · 2025-03-19

    Guo‐Jin Cao

    Read original paper ↗ ·

  26. Local reaction condition optimization via machine learning

    Journal of Molecular Modeling · 2025-04-23

    Wenhuan Song, Honggang Sun

    Read original paper ↗ ·

  27. Construction of a deep - learning - based rehabilitation prediction model for lower-limb motor dysfunction after stroke using synchronous EEG-EMG and fMRI

    Frontiers in Neuroscience · 2025-08-21

    Jian Shi, Hongyu Wang, Haiyan Gou, Yan Chen, He Jia, Youyang Qu, Xinya Wei, Mingyue Fan, Yanlong Wang, Yanmei Zhu, Yulan Zhu

    Read original paper ↗ ·

  28. Dyna-Q Learning Algorithm for Temperature Control in Exothermic Process

    IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) · 2025-08-26

    Yan Yi Vun, Huiyi Xu, Min Keng Tan, Helen Sin Ee Chuo, Heng Jin Tham, Kenneth Tze Kin Teo

    Read original paper ↗ ·

  29. A machine learning framework for long-distance significant wave height prediction in the Yellow Sea with multi-station fusion and interpretability analysis

    Regional Studies in Marine Science · 2026-06-15

    Mengyun Wang, Ling Wang, Cheng Wang, Xindong Zhai

    Read original paper ↗ ·

  30. The Role of Knowledge Representation & Reasoning in Deciphering Chemical Complexity

    Wiley Interdisciplinary Reviews Computational Molecular Science · 2026-09-01

    José Ferraz-Caetano, Filipe Teixeira, M. Natália D. S. Cordeiro

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