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RESEARCH PAPER · CHIMIA International Journal for Chemistry · 2023

How to Accelerate R&D and Optimize Experiment Planning with Machine Learning and Data Science

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

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How to Accelerate R&D and Optimize Experiment Planning with Machine Learning and Data ScienceOriginalHighlights from the 56th Bürgenstock Conference on Stereochemistry 20230Bayesian Optimization as a Sustainable Strategy for Early-Stage Process Development? A Case Study of Cu-Catalyzed C–N Coupling of Sterically Hindered Pyrazines46AI-Powered Innovations in High-Tech Research and Development: From Theory to Practice32Combining Bayesian optimization and automation to simultaneously optimize reaction conditions and routes29Accelerated exploration of heterogeneous CO2 hydrogenation catalysts by Bayesian-optimized high-throughput and automated experimentation56The Lab of the Future: Self-Driving Labs for Molecule Discovery2CIME4R: Exploring iterative, AI-guided chemical reaction optimization campaigns in their parameter space4Application of artificial intelligence in model-based systems engineering of automated production systems4BayBE: a Bayesian Back End for experimental planning in the low-to-no-data regime24Role of the human-in-the-loop in emerging self-driving laboratories for heterogeneous catalysis48Research Trend Analysis in the Field of Self-Driving Labs Using Network Analysis and Topic Modeling1A Helping Hand: A Survey About AI-Driven Experimental Design for Accelerating Scientific Research5Task matrix ‒ a new method of rationing R&D labor0Bayesian Optimization for Resource-Efficient Hydroformylation0

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14 direct citing papers saved.

  1. Highlights from the 56th Bürgenstock Conference on Stereochemistry 2023

    Chemical Science · 2023-01-01

    Marc Reid, Christopher J. Teskey

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  2. Bayesian Optimization as a Sustainable Strategy for Early-Stage Process Development? A Case Study of Cu-Catalyzed C–N Coupling of Sterically Hindered Pyrazines

    ACS Sustainable Chemistry & Engineering · 2023-07-07

    Elena Braconi, Edouard Godineau

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  3. AI-Powered Innovations in High-Tech Research and Development: From Theory to Practice

    Computers, materials & continua/Computers, materials & continua (Print) · 2024-01-01

    Mitra Madanchian, Hamed Taherdoost

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  4. Combining Bayesian optimization and automation to simultaneously optimize reaction conditions and routes

    Chemical Science · 2024-01-01

    Oliver Tobias Schilter, Daniel Pacheco Gutiérrez, Linnea M. Folkmann, Alessandro Castrogiovanni, Alberto García-Durán, Federico Zipoli, Loı̈c M. Roch, Teodoro Laino

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  5. Accelerated exploration of heterogeneous CO2 hydrogenation catalysts by Bayesian-optimized high-throughput and automated experimentation

    Chem Catalysis · 2024-01-21

    Adrián Ramírez, Erwin Lam, Daniel Pacheco Gutiérrez, Yuhui Hou, Hermann Tribukait, Loı̈c M. Roch, Christophe Copéret, Paco V. Laveille

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  6. The Lab of the Future: Self-Driving Labs for Molecule Discovery

    GEN Biotechnology · 2024-04-01

    Sean Ekins

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  7. CIME4R: Exploring iterative, AI-guided chemical reaction optimization campaigns in their parameter space

    Journal of Cheminformatics · 2024-05-10

    Christina Humer, Rachel Nicholls, Henry Heberle, Moritz Heckmann, Michael Pühringer, Thomas Wolf, Maximilian Lübbesmeyer, Julian Heinrich, Julius Hillenbrand, Giulio Volpin, Marc Streit

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  8. Application of artificial intelligence in model-based systems engineering of automated production systems

    Procedia CIRP · 2025-01-01

    Timo Schuchter, Patrick Saft, Ralf Stetter, Markus Pfeil, Wolfram Höpken, Markus Till, Stephan Rudolph

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  9. BayBE: a Bayesian Back End for experimental planning in the low-to-no-data regime

    Digital Discovery · 2025-01-01

    Martin Fitzner, Adrian Šošić, Alexander Vincent Hopp, Marcel Müller, Rim Rihana, Karin Hrovatin, Fabian Liebig, Mathias Winkel, Wolfgang Halter, Jan Gerit Brandenburg

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  10. Role of the human-in-the-loop in emerging self-driving laboratories for heterogeneous catalysis

    Nature Catalysis · 2025-01-29

    Christoph Scheurer, Karsten Reuter

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  11. Research Trend Analysis in the Field of Self-Driving Labs Using Network Analysis and Topic Modeling

    Systems · 2025-04-03

    Woojun Jung, Insung Hwang, Keuntae Cho

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  12. A Helping Hand: A Survey About AI-Driven Experimental Design for Accelerating Scientific Research

    Applied Sciences · 2025-05-07

    Lukas Nolte, Sven Tomforde

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  13. Task matrix ‒ a new method of rationing R&D labor

    Proceedings of the Southwest State University Series Economics Sociology and Management · 2025-10-13

    Olga V. Omelyanskaya, Yuriy Aleksandrovich Vasilev, Пестренин Лев Дмитриевич, Anton V. Vladzymyrskyy

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  14. Bayesian Optimization for Resource-Efficient Hydroformylation

    ACS Catalysis · 2025-11-15

    Lionel Saudan, Anna Xiao Tan, Daniel Pacheco Gutiérrez, Laurent Maggi, Loı̈c M. Roch, Clélia Fantini, Eric Walther, Julien Coulomb, Francesco Santoro

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