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Autonomous mobile robots for exploratory synthetic chemistry

  • Abolhasani, M. & Kumacheva, E. The rise of self-driving labs in chemical and materials sciences. Nat. Synth. 2, 483–492 (2023).

    Article 
    ADS 

    Google Scholar
     

  • Seifrid, M. et al. Autonomous chemical experiments: challenges and perspectives on establishing a self-driving lab. Acc. Chem. Res. 55, 2454–2466 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Angelone, D. et al. Convergence of multiple synthetic paradigms in a universally programmable chemical synthesis machine. Nat. Chem. 13, 63–69 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Steiner, S. et al. Organic synthesis in a modular robotic system driven by a chemical programming language. Science 363, eaav2211 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Coley, C. W. et al. A robotic platform for flow synthesis of organic compounds informed by AI planning. Science 365, eaax1566 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Chatterjee, S., Guidi, M., Seeberger, P. H. & Gilmore, K. Automated radial synthesis of organic molecules. Nature 579, 379–384 (2020).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Bennett, J. A. et al. Autonomous reaction Pareto-front mapping with a self-driving catalysis laboratory. Nat. Chem. Eng. 1, 240–250 (2024).

    Article 

    Google Scholar
     

  • Wang, J. Y. et al. Identifying general reaction conditions by bandit optimization. Nature 626, 1025–1033 (2024).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Szymanski, N. J. et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature 624, 86–91 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ha, T. et al. AI-driven robotic chemist for autonomous synthesis of organic molecules. Sci. Adv. 9, eadj0461 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Burger, B. et al. A mobile robotic chemist. Nature 583, 237–241 (2020).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Zhu, Q. et al. An all-round AI-Chemist with a scientific mind. Natl Sci. Rev. 9, nwac190 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhu, Q. et al. Automated synthesis of oxygen-producing catalysts from Martian meteorites by a robotic AI chemist. Nat. Synth. 3, 319–328 (2023).

    Article 
    ADS 

    Google Scholar
     

  • Koscher, B. A. et al. Autonomous, multiproperty-driven molecular discovery: from predictions to measurements and back. Science 382, eadi1407 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Bayley, O., Savino, E., Slattery, A. & Noël, T. Autonomous chemistry: navigating self-driving labs in chemical and material sciences. Matter 7, 2382–2398 (2024).

    Article 
    CAS 

    Google Scholar
     

  • Caramelli, D. et al. Discovering new chemistry with an autonomous robotic platform driven by a reactivity-seeking neural network. ACS Cent. Sci. 7, 1821–1830 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Porwol, L. et al. An autonomous chemical robot discovers the rules of inorganic coordination chemistry without prior knowledge. Angew. Chem. Int. Ed. 59, 11256–11261 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Leeman, J. et al. Challenges in high-throughput inorganic materials prediction and autonomous synthesis. PRX Energy 3, 011002 (2024).

    Article 

    Google Scholar
     

  • Blair, D. J. et al. Automated iterative Csp3–C bond formation. Nature 604, 92–97 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Angello, N. H. et al. Closed-loop optimization of general reaction conditions for heteroaryl Suzuki–Miyaura coupling. Science 378, 399–405 (2022).

    Article 
    ADS 
    MathSciNet 
    CAS 
    PubMed 

    Google Scholar
     

  • MacLeod, B. P. et al. Self-driving laboratory for accelerated discovery of thin-film materials. Sci. Adv. 6, eaaz8867 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jiang, Y. et al. An artificial intelligence enabled chemical synthesis robot for exploration and optimization of nanomaterials. Sci. Adv. 8, eabo2626 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Slattery, A. et al. Automated self-optimization, intensification, and scale-up of photocatalysis in flow. Science 383, eadj1817 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Christensen, M. et al. Data-science driven autonomous process optimization. Commun. Chem. 4, 112 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Basford, A. R. et al. Streamlining the automated discovery of porous organic cages. Chem. Sci. 15, 6331–6348 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mennen, S. M. et al. The evolution of high-throughput experimentation in pharmaceutical development and perspectives on the future. Org. Process Res. Dev. 23, 1213–1242 (2019).

    Article 
    CAS 

    Google Scholar
     

  • Ronchetti, R., Moroni, G., Carotti, A., Gioiello, A. & Camaioni, E. Recent advances in urea- and thiourea-containing compounds: focus on innovative approaches in medicinal chemistry and organic synthesis. RSC Med. Chem. 12, 1046–1064 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Brown, D. G. & Boström, J. Analysis of past and present synthetic methodologies on medicinal chemistry: where have all the new reactions gone?: Miniperspective. J. Med. Chem. 59, 4443–4458 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zhang, D., Ronson, T. K., Zou, Y.-Q. & Nitschke, J. R. Metal–organic cages for molecular separations. Nat. Rev. Chem. 5, 168–182 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Bilbeisi, R. A. et al. Subcomponent self-assembly and guest-binding properties of face-capped Fe4L48+ capsules. J. Am. Chem. Soc. 134, 5110–5119 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Jiménez, A. et al. Selective encapsulation and sequential release of guests within a self-sorting mixture of three tetrahedral cages. Angew. Chem. Int. Ed. 53, 4556–4560 (2014).

    Article 

    Google Scholar
     

  • Yoshida, N. & Ichikawa, K. Synthesis and structure of a dinuclear zinc(II) triple helix of an N,N-bis-bidentate Schiff base: new building blocks for the construction of helical structures. Chem. Commun. https://doi.org/10.1039/a701669g (1997).

  • Chu, L., Ohta, C., Zuo, Z. & MacMillan, D. W. C. Carboxylic acids as a traceless activation group for conjugate additions: a three-step synthesis of (±)-pregabalin. J. Am. Chem. Soc. 136, 10886–10889 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Vijayakrishnan, S., Ward, J. W. & Cooper, A. I. Discovery of a covalent triazine framework photocatalyst for visible-light-driven chemical synthesis using high-throughput screening. ACS Catal. 12, 10057–10064 (2022).

    Article 
    CAS 

    Google Scholar
     

  • Thurow, K. et al. Multi-floor laboratory transportation technologies based on intelligent mobile robots. Transp. Saf. Environ. 1, 37–53 (2019).

    Article 

    Google Scholar
     

  • Grau, A., Indri, M., Lo Bello, L. & Sauter, T. Robots in industry: the past, present, and future of a growing collaboration with humans. IEEE Ind. Electron. Mag. 15, 50–61 (2021).

    Article 

    Google Scholar
     

  • Laveille, P. et al. Swiss CAT+, a data-driven infrastructure for accelerated catalysts discovery and optimization. CHIMIA 77, 154 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Boiko, D. A., MacKnight, R., Kline, B. & Gomes, G. Autonomous chemical research with large language models. Nature 624, 570–578 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Darvish, K. et al. ORGANA: a robotic assistant for automated chemistry experimentation and characterization. Preprint at https://arxiv.org/abs/2401.06949 (2024).

  • Giorgino, T. Computing and visualizing dynamic time warping alignments in R: the dtw package. J. Stat. Softw. 31, 1–24 (2009).

  • Dolomanov, O. V., Bourhis, L. J., Gildea, R. J., Howard, J. A. K. & Puschmann, H. OLEX2: a complete structure solution, refinement and analysis program. J. Appl. Crystallogr. 42, 339–341 (2009).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Sheldrick, G. M. SHELXT—integrated space-group and crystal-structure determination. Acta Crystallogr. 71, 3–8 (2015).


    Google Scholar
     

  • Sheldrick, G. M. Crystal structure refinement with SHELXL. Acta Crystallogr. C 71, 3–8 (2015).

    Article 
    ADS 

    Google Scholar
     

  • Ayme, J.-F., Cooper, A. I., Szczypiński, F. T. & Vijayakrishnan, S. Data and code examples for: Twin cooperative mobile robots for autonomous synthetic chemistry. Zenodo https://doi.org/10.5281/zenodo.11209807 (2024).

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