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RESEARCH PAPER · The Journal of Physical Chemistry Letters · 2019

Visualizing Scientists’ Cognitive Representation of Materials Data through the Application of Ontology

Lauren Takahashi, KEISUKE G. TAKAHASHI

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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
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Visualizing Scientists’ Cognitive Representation of Materials Data through the Application of OntologyOriginalHigh-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery179Property-Oriented Material Design Based on a Data-Driven Machine Learning Technique89Constructing catalyst knowledge networks from catalyst big data in oxidative coupling of methane for designing catalysts26An Ontology-Based Approach to Enable Data-Driven Research in the Field of NDT in Civil Engineering15Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective46The Rise of Catalysts Informatics1The materials tetrahedron has a “digital twin”34A Perspective on Digital Knowledge Representation in Materials Science and Engineering40Data in Materials and Catalysts Informatics4Big Data in a Nano World: A Review on Computational, Data-Driven Design of Nanomaterials Structures, Properties, and Synthesis83Catalysts informatics: paradigm shift towards data-driven catalyst design61The value of negative results in data-driven catalysis research72Toward the Golden Age of Materials Informatics: Perspective and Opportunities23Structuring superconductor data with ontology: reproducing historical datasets as knowledge bases4Standardizing chemical compounds with language models4Molecular Simulation Meets Machine Learning15Unsupervised Machine Learning and Beyond Machine Learning0Semantic integration of diverse data in materials science: Assessing Orowan strengthening14Natural Language Processing-Driven Microscopy Ontology Development6AI and automation: democratizing automation and the evolution towards true AI-autonomous robotics11Digitalisation of catalytic processes for sustainable production of biobased chemicals and exploration of wider chemical space8The Problem of Competing Scientific Ontologies in Materials Knowledge Representation0Knowledge graphs in heterogeneous catalysis: Recent advances and future opportunities7Statistical and Predictive Analysis of Supported Catalytically Active Metal Solutions (SCALMS) in Propane Dehydrogenation2From Data to Discovery: Challenges and Solutions in AI-Driven Materials Science0FAIR data in mechanical testing of polymers: Implementation of the full-notch creep test ontology (OntoFNCT)2

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  1. High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery

    Physical Chemistry Chemical Physics · 2020-01-01

    Katherine McCullough, Travis Williams, Kathleen Mingle, Pooyan Jamshidi, Jochen A. Lauterbach

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  2. Property-Oriented Material Design Based on a Data-Driven Machine Learning Technique

    The Journal of Physical Chemistry Letters · 2020-04-24

    Qionghua Zhou, Shuaihua Lu, Yilei Wu, Jinlan Wang

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  3. Constructing catalyst knowledge networks from catalyst big data in oxidative coupling of methane for designing catalysts

    Chemical Science · 2021-01-01

    Lauren Takahashi, Thanh Nhat Nguyen, Sunao Nakanowatari, Aya Fujiwara, Toshiaki Taniike, KEISUKE G. TAKAHASHI

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  4. An Ontology-Based Approach to Enable Data-Driven Research in the Field of NDT in Civil Engineering

    Remote Sensing · 2021-06-21

    Benjamí Moreno Torres, Christoph Völker, Sarah Mandy Nagel, Thomas Hanke, Sabine Kruschwitz

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  5. Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective

    Catalysis Science & Technology · 2022-01-01

    Annette Trunschke

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  6. The Rise of Catalysts Informatics

    Springer eBooks · 2022-01-01

    KEISUKE G. TAKAHASHI, Lauren Takahashi, Shun Nishimura, Jun Fujima, Junya Ohyama

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  7. The materials tetrahedron has a “digital twin”

    MRS Bulletin · 2022-02-01

    Michael E. Deagen, L. Catherine Brinson, Richard A. Vaia, Linda S. Schadler

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  8. A Perspective on Digital Knowledge Representation in Materials Science and Engineering

    Advanced Engineering Materials · 2022-04-12

    Bernd Bayerlein, Thomas Hanke, Thilo Muth, Jens Riedel, Markus Schilling, Christoph Schweizer, Birgit Skrotzki, Alexandru Todor, Benjamí Moreno Torres, Jörg F. Unger, Christoph Völker, Jürgen Olbricht

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  9. Data in Materials and Catalysts Informatics

    ACS symposium series · 2022-06-14

    KEISUKE G. TAKAHASHI, Lauren Takahashi

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  10. Big Data in a Nano World: A Review on Computational, Data-Driven Design of Nanomaterials Structures, Properties, and Synthesis

    ACS Nano · 2022-11-15

    Ruoxi Yang, Caitlin A. McCandler, Oxana Andriuc, Martin Siron, Rachel Woods‐Robinson, Matthew K. Horton, Kristin Aslaug Persson

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  11. Catalysts informatics: paradigm shift towards data-driven catalyst design

    Chemical Communications · 2023-01-01

    KEISUKE G. TAKAHASHI, Junya Ohyama, Shun Nishimura, Jun Fujima, Lauren Takahashi, Takeaki Uno, Toshiaki Taniike

    Read original paper ↗ ·

  12. The value of negative results in data-driven catalysis research

    Nature Catalysis · 2023-02-27

    Toshiaki Taniike, KEISUKE G. TAKAHASHI

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  13. Toward the Golden Age of Materials Informatics: Perspective and Opportunities

    The Journal of Physical Chemistry Letters · 2023-05-12

    KEISUKE G. TAKAHASHI, Lauren Takahashi

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  14. Structuring superconductor data with ontology: reproducing historical datasets as knowledge bases

    Science and Technology of Advanced Materials Methods · 2023-06-21

    Masashi Ishii, Koichi Sakamoto

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  15. Standardizing chemical compounds with language models

    Machine Learning Science and Technology · 2023-07-18

    Miruna T. Cretu, Alessandra Toniato, Amol Thakkar, Amin Debabeche, Teodoro Laino, Alain C. Vaucher

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  16. Molecular Simulation Meets Machine Learning

    Journal of Chemical & Engineering Data · 2023-12-19

    Richard J. Sadus

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  17. Unsupervised Machine Learning and Beyond Machine Learning

    Springer eBooks · 2023-12-29

    KEISUKE G. TAKAHASHI, Lauren Takahashi

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  18. Semantic integration of diverse data in materials science: Assessing Orowan strengthening

    Scientific Data · 2024-04-30

    Bernd Bayerlein, Markus Schilling, Philipp v. Hartrott, Jörg Waitelonis

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  19. Natural Language Processing-Driven Microscopy Ontology Development

    Integrating materials and manufacturing innovation · 2024-11-11

    Bernd Bayerlein, Markus Schilling, Maurice Curran, Carelyn E. Campbell, Alden A. Dima, Henk Birkholz, June W. Lau

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  20. AI and automation: democratizing automation and the evolution towards true AI-autonomous robotics

    Chemical Science · 2025-01-01

    Lauren Takahashi, Micke Kuwahara, KEISUKE G. TAKAHASHI

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  21. Digitalisation of catalytic processes for sustainable production of biobased chemicals and exploration of wider chemical space

    Catalysis Science & Technology · 2025-01-01

    Firdaus Parveen, Anna Grace Slater

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  22. The Problem of Competing Scientific Ontologies in Materials Knowledge Representation

    Journal of Artificial Intelligence for Materials Science · 2025-01-01

    Ivan Petrov, Olga Ivanova, Dmitry Smirnov

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  23. Knowledge graphs in heterogeneous catalysis: Recent advances and future opportunities

    Chinese Journal of Chemical Engineering · 2025-07-02

    Raúl Díaz, Hongliang Xin

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  24. Statistical and Predictive Analysis of Supported Catalytically Active Metal Solutions (SCALMS) in Propane Dehydrogenation

    ChemCatChem · 2025-08-27

    Angelika Owsienko, Philipp Stangl, Nnamdi Madubuko, Richard Lenz, Marco Haumann

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  25. From Data to Discovery: Challenges and Solutions in AI-Driven Materials Science

    Journal of the Physical Society of Japan · 2026-03-25

    Hieu‐Chi Dam, Hiori Kino, Takashi Miyake

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  26. FAIR data in mechanical testing of polymers: Implementation of the full-notch creep test ontology (OntoFNCT)

    Results in Engineering · 2026-04-16

    Markus Schilling, Niklas Marschall, Ute Niebergall, Martin Böhning

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