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RESEARCH PAPER · Journal of Chemical Information and Modeling · 2019

Intuition-Enabled Machine Learning Beats the Competition When Joint Human-Robot Teams Perform Inorganic Chemical Experiments

Vasilios Duros, Jonathan Grizou, Abhishek Sharma, S. Hessam M. Mehr, Andrius Bubliauskas, Przemysław Frei, Haralampos N. Miras, Leroy Cronin

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42 direct citing works

Intuition-Enabled Machine Learning Beats the Competition When Joint Human-Robot Teams Perform Inorganic Chemical ExperimentsOriginalUniversal Chemical Synthesis and Discovery with ‘The Chemputer’154Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt12Autonomous Discovery in the Chemical Sciences Part I: Progress317Assessing the impact of deviations in optimized multistep flow synthesis on the scale-up8Opportunities and Challenges for Machine Learning in Materials Science523Can Machines “Learn” Halide Perovskite Crystal Formation without Accurate Physicochemical Features?12Coordination Chemistry in Polyoxometalates and Metal Clusters2Elevating Chemistry Research with a Modern Electronics Toolkit82Intelligent Microfluidics: The Convergence of Machine Learning and Microfluidics in Materials Science and Biomedicine189Lead optimization5Failure-Experiment-Supported Optimization of Poorly-Reproducible Synthetic Conditions for Novel Lanthanide Metal-Organic Frameworks0Autonomous experimentation systems for materials development: A community perspective398Chemputation and the Standardization of Chemical Informatics73Failure‐Experiment‐Supported Optimization of Poorly Reproducible Synthetic Conditions for Novel Lanthanide Metal‐Organic Frameworks with Two‐Dimensional Secondary Building Units**12Intuitiveness in Active Teaching3Data-driven efficient synthetic exploration of anionic lanthanide-based metal–organic frameworks6Active meta-learning for predicting and selecting perovskite crystallization experiments24Machine Learning in Structural Design: An Opinionated Review89The case for data science in experimental chemistry: examples and recommendations83Networks and interfaces as catalysts for polymer materials innovation10Data-driven design of molecular nanomagnets42Bayesian optimization of the composition of the lanthanide metal–organic framework MIL-103 for white-light emission11Real-Time Algorithm Recommendation Using Meta-Learning1Tuning Bayesian optimization for materials synthesis: simulating two- and three-dimensional cases10Robotic Modules for the Programmable Chemputation of Molecules and Materials38In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science99Harnessing Medicinal Chemical Intuition from Collective Intelligence1Discovery of Ternary Antimonides A–Al–Sb (A = Rb or Cs) with Desired Structural Motifs Guided by Machine Learning5Intérêt de la prise en compte des variabilités de l’activité et de l’acceptabilité dans le cadre d’une conception centrée utilisateurs des situations de travail collaboratives Humain-Robot0Accelerating Materials Discovery for Polymer Solar Cells: Data-Driven Insights Enabled by Natural Language Processing10Autonomous Size-Targeting for Block Copolymer Nanoparticles9Harnessing Medicinal Chemical Intuition from Collective Intelligence6The Alchemist, the Scientist, and the Robot: Exploring the Potential of Human‐AI Symbiosis in Self‐Driving Polymer Laboratories8Motion2Press: Cross Model Learning from IMU to Plantar Pressure for Gait Analysis6Composite robotic system for intelligent chemical experiment operations based on skill acquisition and knowledge graphs2Data‐Driven Polyoxometalate Chemistry5A comprehensive understanding of the use of machine learning in polyoxometalates0SPACESHIP: Autonomous Mapping of Hardware-Dependent Synthesizable Space in Solution-Phase Gold Nanomaterials0Organic Materials of Tomorrow: Horizons of Artificial Intelligence0

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  1. Universal Chemical Synthesis and Discovery with ‘The Chemputer’

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  3. Autonomous Discovery in the Chemical Sciences Part I: Progress

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  4. Assessing the impact of deviations in optimized multistep flow synthesis on the scale-up

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  5. Opportunities and Challenges for Machine Learning in Materials Science

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  6. Can Machines “Learn” Halide Perovskite Crystal Formation without Accurate Physicochemical Features?

    The Journal of Physical Chemistry C · 2020-05-26

    Ian M. Pendleton, Mary Kathleen Caucci, Michael Tynes, Aaron Dharna, Mansoor Ani Najeeb, Zhi Li, Emory M. Chan, Alexander J. Norquist, Joshua Schrier

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  7. Coordination Chemistry in Polyoxometalates and Metal Clusters

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  8. Elevating Chemistry Research with a Modern Electronics Toolkit

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  9. Intelligent Microfluidics: The Convergence of Machine Learning and Microfluidics in Materials Science and Biomedicine

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  10. Lead optimization

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  11. Failure-Experiment-Supported Optimization of Poorly-Reproducible Synthetic Conditions for Novel Lanthanide Metal-Organic Frameworks

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    Yu Kitamura, Emi Terado, Zechen Zhang, Hirofumi Yoshikawa, Tomoko Inose, Hiroshi Uji‐i, Masaharu Tanimizu, Akihiro Inokuchi, Yoshinobu Kamakura, Daisuke Tanaka

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  12. Autonomous experimentation systems for materials development: A community perspective

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    Eric A. Stach, Brian DeCost, Aaron Gilad Kusne, Jason R. Hattrick-Simpers, Keith A. Brown, Kristofer G. Reyes, Joshua Schrier, Simon J. L. Billinge, Tonio Buonassisi, Ian T Foster, Carla Pedro Gomes, John M. Gregoire, Apurva Mehta, Joseph H. Montoya, Elsa Olivetti, Chiwoo Park, Eli Rotenberg, Semion K. Saikin, Sylvia Smullin, Valentin Stanev, Benji Maruyama

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  13. Chemputation and the Standardization of Chemical Informatics

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    Alexander J. S. Hammer, Artem I. Leonov, Nicola L. Bell, Leroy Cronin

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  14. Failure‐Experiment‐Supported Optimization of Poorly Reproducible Synthetic Conditions for Novel Lanthanide Metal‐Organic Frameworks with Two‐Dimensional Secondary Building Units**

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  15. Intuitiveness in Active Teaching

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  16. Data-driven efficient synthetic exploration of anionic lanthanide-based metal–organic frameworks

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  17. Active meta-learning for predicting and selecting perovskite crystallization experiments

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  18. Machine Learning in Structural Design: An Opinionated Review

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  19. The case for data science in experimental chemistry: examples and recommendations

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  20. Networks and interfaces as catalysts for polymer materials innovation

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  21. Data-driven design of molecular nanomagnets

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  22. Bayesian optimization of the composition of the lanthanide metal–organic framework MIL-103 for white-light emission

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  23. Real-Time Algorithm Recommendation Using Meta-Learning

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  25. Robotic Modules for the Programmable Chemputation of Molecules and Materials

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  38. SPACESHIP: Autonomous Mapping of Hardware-Dependent Synthesizable Space in Solution-Phase Gold Nanomaterials

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