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RESEARCH PAPER · Materials Discovery · 2016

Material informatics driven design and experimental validation of lead titanate as an aqueous solar photocathode

Taylor Moot, Olexandr Isayev, Robert W. Call, Shannon M. McCullough, Morgan Zemaitis, René López, James F. Cahoon, Alexander Tropsha

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

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42 direct citing works

Material informatics driven design and experimental validation of lead titanate as an aqueous solar photocathodeOriginalIncluding crystal structure attributes in machine learning models of formation energies via Voronoi tessellations412Computer-aided design of metal chalcohalide semiconductors: from chemical composition to crystal structure70The dye-sensitized solar cell database71Less is more: Sampling chemical space with active learning802Machine learning for molecular and materials science4894Efficiency of Generalized Regular k -point grids61Data mining new energy materials from structure databases57Toward Design of Novel Materials for Organic Electronics167Generalized regular k-point grid generation on the fly30Predicting structure zone diagrams for thin film synthesis by generative machine learning60Topology-Based Machine Learning Strategy for Cluster Structure Prediction46Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery281High‐throughput computational materials screening and discovery of optoelectronic semiconductors100Development Status and Prospects of Artificial Intelligence in the Field of Energy Conversion Materials14Organic Chromophores Designed for Hole Injection into Wide-Band-Gap Metal Oxides for Solar Fuel Applications15Machine Learning Regression Guided Thermoelectric Materials Discovery – A Review44Environmental Applications of Nanotechnology: Nano-enabled Remediation Processes in Water, Soil and Air Treatment50Dye-Sensitized Nonstoichiometric Strontium Titanate Core–Shell Photocathodes for Photoelectrosynthesis Applications6Accelerated Discovery of the Polymer Matrix for Cartilage Repair Through Machine Learning Algorithms0Machine learning workflow for microparticle composite thin-film process–structure linkages4Molecular representations for machine learning applications in chemistry93A compendium and meta-analysis of flatband potentials for TiO2, ZnO, and SnO2 semiconductors in aqueous media60Photoanode Modified by PbTiO3 or PbTiO3/TiO2 Nanofibers in Dye-Sensitized Solar Cell7Accelerated Discovery of the Polymer Blends for Cartilage Repair through Data-Mining Tools and Machine-Learning Algorithm18In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science99Towards machine learning assisted discovery of organic topological materials0Reconstructing Solar Materials Innovation Through Data Science: A Review of Opportunities and Structural Bottlenecks2

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27 direct citing papers saved.

  1. Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations

    Physical review. B./Physical review. B · 2017-07-14

    Logan T. Ward, Ruoqian Liu, Amar Krishna, Vinay I. Hegde, Ankit Agrawal, Alok Choudhary, Chris M. Wolverton

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  2. Computer-aided design of metal chalcohalide semiconductors: from chemical composition to crystal structure

    Chemical Science · 2017-12-04

    Daniel W. Davies, Keith T. Butler, Jonathan Michael Skelton, Congwei Xie, Артем Ромаевич Оганов, Aron Walsh

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  3. The dye-sensitized solar cell database

    Journal of Cheminformatics · 2018-04-03

    Vishwesh Venkatraman, Rajesh Raju, Solon P. Oikonomopoulos, Bjørn Kåre Alsberg

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  4. Less is more: Sampling chemical space with active learning

    The Journal of Chemical Physics · 2018-05-22

    Justin S. Smith, Benjamin T. Nebgen, Nicholas Lubbers, Olexandr Isayev, Adrián E. Roitberg

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  5. Machine learning for molecular and materials science

    Nature · 2018-07-01

    Keith T. Butler, Daniel W. Davies, Hugh Cartwright, Olexandr Isayev, Aron Walsh

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  6. Efficiency of Generalized Regular k -point grids

    Computational Materials Science · 2018-07-20

    Wiley S. Morgan, Jeremy J. Jorgensen, Bret C. Hess, Gus L. W. Hart

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  7. Data mining new energy materials from structure databases

    Renewable and Sustainable Energy Reviews · 2019-03-25

    Lei Zhang, Zhiqiao Chen, Jing Su, Jingfa Li

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  8. Toward Design of Novel Materials for Organic Electronics

    Advanced Materials · 2019-04-22

    Pascal Friederich, Artem Fediai, Simon Kaiser, Manuel Konrad, Nicole Jung, Wolfgang Wenzel

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  9. Generalized regular k-point grid generation on the fly

    Computational Materials Science · 2019-12-02

    Wiley S. Morgan, John Eric Christensen, Parker K. Hamilton, Jeremy J. Jorgensen, Branton J. Campbell, Gus L. W. Hart, Rodney W. Forcade

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  10. Predicting structure zone diagrams for thin film synthesis by generative machine learning

    Communications Materials · 2020-03-26

    Lars Banko, Yury V. Lysogorskiy, Dario Grochla, Dennis Naujoks, Ralf Drautz, Alfred Ludwig

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  11. Topology-Based Machine Learning Strategy for Cluster Structure Prediction

    The Journal of Physical Chemistry Letters · 2020-04-22

    Xin Chen, Dong Chen, Mouyi Weng, Yi Jiang, Guo‐Wei Wei, Feng Ming Pan

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  12. Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery

    Physical Review Materials · 2020-06-01

    Cheol Woo Park, Chris Wolverton

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  13. High‐throughput computational materials screening and discovery of optoelectronic semiconductors

    Wiley Interdisciplinary Reviews Computational Molecular Science · 2020-06-29

    Shulin Luo, Tianshu Li, Xinjiang Wang, Muhammad Faizan, Lijun Zhang

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  14. Development Status and Prospects of Artificial Intelligence in the Field of Energy Conversion Materials

    Frontiers in Energy Research · 2020-07-31

    Xinyi Yang, Ziyi Luo, Zihan Huang, Yihang Zhao, Zhichang Xue, Yingxue Wang, Wei‐Di Liu, Siliang Liu, Hong Zhang, Kui Xu, Shuming Dou, Jie Xu, Wenbin Hu, Yida Deng

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  15. Organic Chromophores Designed for Hole Injection into Wide-Band-Gap Metal Oxides for Solar Fuel Applications

    Chemistry of Materials · 2020-10-01

    Linda Nhon, Aaron D. Taggart, Taylor Moot, M. Kyle Brennaman, Pradeepkumar Jagadesan, Kirk S. Schanze, James F. Cahoon, John R. Reynolds

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  16. Machine Learning Regression Guided Thermoelectric Materials Discovery – A Review

    ES Materials & Manufacturing · 2021-01-01

    Guangshuai Han, Yixuan Sun, Yining Feng, Guang Lin, Na Lu

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  17. Environmental Applications of Nanotechnology: Nano-enabled Remediation Processes in Water, Soil and Air Treatment

    Water Air & Soil Pollution · 2021-02-01

    Stuart Linley, Neil R. Thomson

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  18. Dye-Sensitized Nonstoichiometric Strontium Titanate Core–Shell Photocathodes for Photoelectrosynthesis Applications

    ACS Applied Materials & Interfaces · 2021-03-22

    Caroline E. Reilly, Robert J. Dillon, Animesh Nayak, Shane Brogan, Taylor Moot, M. Kyle Brennaman, René López, Thomas J. Meyer, Leila Alibabaei

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  19. Accelerated Discovery of the Polymer Matrix for Cartilage Repair Through Machine Learning Algorithms

    Research Square · 2021-06-04

    Anusha Mairpady, Abdel‐Hamid I. Mourad, Mohammad Sayem Mozumder

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  20. Machine learning workflow for microparticle composite thin-film process–structure linkages

    Journal of Coatings Technology and Research · 2021-08-06

    Peter R. Griffiths, Tequila A. L. Harris

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  21. Molecular representations for machine learning applications in chemistry

    International Journal of Quantum Chemistry · 2021-12-27

    Shampa Raghunathan, U. Deva Priyakumar

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  22. A compendium and meta-analysis of flatband potentials for TiO2, ZnO, and SnO2 semiconductors in aqueous media

    Chemical Physics Reviews · 2022-01-10

    Milan Y. Patel, Michael J. Mortelliti, Jillian Lee Dempsey

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  23. Photoanode Modified by PbTiO3 or PbTiO3/TiO2 Nanofibers in Dye-Sensitized Solar Cell

    IEEE Transactions on Electron Devices · 2022-01-29

    Yu–Hsun Nien, Yi-Ting Wu, Jung-Chuan Chou, Chih-Sung Ho, Chih-Hsien Lai, Po‐Yu Kuo, Zhen-Rong Yong, Yu‐Che Lin, Ruei-Hong Syu

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  24. Accelerated Discovery of the Polymer Blends for Cartilage Repair through Data-Mining Tools and Machine-Learning Algorithm

    Polymers · 2022-04-28

    Anusha Mairpady, Abdel‐Hamid I. Mourad, Mohammad Sayem Mozumder

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  25. In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science

    Journal of the American Chemical Society · 2023-09-27

    Joshua Schrier, Alexander J. Norquist, Tonio Buonassisi, Jakoah Brgoch

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  26. Towards machine learning assisted discovery of organic topological materials

    Nano Futures · 2025-10-30

    Muhammad Usman

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  27. Reconstructing Solar Materials Innovation Through Data Science: A Review of Opportunities and Structural Bottlenecks

    Energy Technology · 2026-06-01

    Khadijah Mohammedsaleh Katubi, Sumaira Naeem, Norah Alomayrah, Mohammed Sultan Al-Buriahi

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