{"id":552238,"date":"2026-07-31T01:38:12","date_gmt":"2026-07-31T01:38:12","guid":{"rendered":"https:\/\/www.newsbeep.com\/nz\/552238\/"},"modified":"2026-07-31T01:38:12","modified_gmt":"2026-07-31T01:38:12","slug":"the-past-present-and-future-of-self-driving-laboratories","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/nz\/552238\/","title":{"rendered":"The past, present and future of self-driving laboratories"},"content":{"rendered":"

Kalinin, S. V. et al. Automated and autonomous experiments in electron and scanning probe microscopy. ACS Nano 15, 12604\u201312627 (2021).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kusne, A. G. et al. On-the-fly closed-loop materials discovery via Bayesian active learning. Nat. Commun. 11, 5966 (2020).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Darvish, K. et al. ORGANA: a robotic assistant for automated chemistry experimentation and characterization. Matter 8, 101897 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Pyzer-Knapp, E. O. et al. Accelerating materials discovery using artificial intelligence, high performance computing and robotics. Npj Comput. Mater. 8, 84 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Abolhasani, M. & Kumacheva, E. The rise of self-driving labs in chemical and materials sciences. Nat. Synth. 2, 483\u2013492 (2023).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Epps, R. W., Volk, A. A., Ibrahim, M. Y. S. & Abolhasani, M. Universal self-driving laboratory for accelerated discovery of materials and molecules. Chem 7, 2541\u20132545 (2021).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Delgado-Licona, F., Addington, D., Alsaiari, A. & Abolhasani, M. Engineering principles for self-driving laboratories. Nat. Chem. Eng. 2, 277\u2013280 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Li, J. et al. Autonomous microfluidic experimentation for exploring reaction inference and synthesizing double perovskite nanoplatelets. Nat. Commun.\u00a017, 4514 (2026).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Delgado-Licona, F. et al. Flow-driven data intensification to accelerate autonomous inorganic materials discovery. Nat. Chem. Eng. 2, 436\u2013446 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Epps, R. W. et al. Artificial chemist: an autonomous quantum dot synthesis bot. Adv. Mater. 32, 2001626 (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bennett, J. A. et al. Autonomous reaction pareto-front mapping with a self-driving catalysis laboratory. Nat. Chem. Eng. 1, 240\u2013250 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Burger, B. et al. A mobile robotic chemist. Nature 583, 237\u2013241 (2020).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Gongora, A. E. et al. A Bayesian experimental autonomous researcher for mechanical design. Sci. Adv. 6, eaaz1708 (2020).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Szymanski, N. J. et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature 624, 86\u201391 (2023).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

MacLeod, B. P. et al. Self-driving laboratory for accelerated discovery of thin-film materials. Sci. Adv. 6, eaaz8867 (2020).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Slattery, A. et al. Automated self-optimization, intensification, and scale-up of photocatalysis in flow. Science 383, eadj1817 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Boiko, D. A., MacKnight, R., Kline, B. & Gomes, G. Autonomous chemical research with large language models. Nature 624, 570\u2013578 (2023).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Steiner, S. et al. Organic synthesis in a modular robotic system driven by a chemical programming language. Science 363, eaav2211 (2019).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Koscher, B. A. et al. Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back. Science 382, eadi1407 (2023).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Volk, A. A. et al. AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning. Nat. Commun. 14, 1403 (2023).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Xu, J. et al. Autonomous multi-robot synthesis and optimization of metal halide perovskite nanocrystals. Nat. Commun. 16, 7841 (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bennett, J. A. et al. An autonomous lab for data-driven homogeneous catalysis. Nat. Commun. https:\/\/doi.org\/10.1038\/s41467-026-74425-x<\/a> (2026).<\/p>\n

Canty, R. B. & Abolhasani, M. Reproducibility in automated chemistry laboratories using computer science abstractions. Nat. Synth. 3, 1327\u20131339 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Coley, C. W., Eyke, N. S. & Jensen, K. F. Autonomous discovery in the chemical sciences part I: progress. Angew. Chem. Int. Ed. 59, 22858\u201322893 (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Gromski, P. S., Henson, A. B., Granda, J. M. & Cronin, L. How to explore chemical space using algorithms and automation. Nat. Rev. Chem. 3, 119\u2013128 (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bennett, J. A. & Abolhasani, M. Autonomous chemical science and engineering enabled by self-driving laboratories. Curr. Opin. Chem. Eng. 36, 100831 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Montoya, J. H. et al. Toward autonomous materials research: recent progress and future challenges. Appl. Phys. Rev. 9, 011405 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Seifrid, M. et al. Autonomous chemical experiments: challenges and perspectives on establishing a self-driving lab. Acc. Chem. Res. 55, 2454\u20132466 (2022).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Christensen, M. et al. Automation isn\u2019t automatic. Chem. Sci. 12, 15473\u201315490 (2021).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Gil, D. & Moler, K. A. Accelerating science with AI. Science 390, 965\u2013965 (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

M. J. Kratsios, D. O. Sacks, & M. A. Rubio. America\u2019s AI Action Plan (The White House, 2025).<\/p>\n

Torres-Acosta, M. A., Lye, G. J. & Dikicioglu, D. Automated liquid-handling operations for robust, resilient, and efficient bio-based laboratory practices. Biochem. Eng. J. 188, 108713 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Harre, M., Tilstam, U. & Weinmann, H. Breaking the new bottleneck: automated synthesis in chemical process research and development. Org. Process. Res. Dev. 3, 304\u2013318 (1999).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Waltz, D. & Buchanan, B. G. Automating science. Science 324, 43\u201344 (2009).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kim, Y. et al. Self-driving laboratories with artificial intelligence: an overview of process systems engineering perspective. Comput. Chem. Eng. 203, 109266 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Tobias, A. V. & Wahab, A. Autonomous \u2018self-driving\u2019 laboratories: a review of technology and policy implications. R. Soc. Open. Sci. https:\/\/doi.org\/10.1098\/rsos.250646<\/a> (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Tom, G. et al. Self-driving laboratories for chemistry and materials science. Chem. Rev. 124, 9633\u20139732 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lindsey, J. S. A retrospective on the automation of laboratory synthetic chemistry. Chemom. Intell. Lab. Syst. 17, 15\u201345 (1992).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Andrew Corkan, L. & Lindsey, J. S. Experiment manager software for an automated chemistry workstation, including a scheduler for parallel experimentation. Chemom. Intell. Lab. Syst. 17, 47\u201374 (1992).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Plouvier, J.-C., Andrew Corkan, L. & Lindsey, J. S. Experiment planner for strategic experimentation with an automated chemistry workstation. Chemom. Intell. Lab. Syst. 17, 75\u201394 (1992).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Andrew Corkan, L., Plouvier, J.-C. & Lindsey, J. S. Application of an automated chemistry workstation to problems in synthetic chemistry. Chemom. Intell. Lab. Syst. 17, 95\u2013105 (1992).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

King, R. D. et al. The automation of science. Science 324, 85\u201389 (2009).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Angello, N. H. et al. Closed-loop optimization of general reaction conditions for heteroaryl suzuki-miyaura coupling. Science 378, 399\u2013405 (2022).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Orouji, N. et al. Autonomous catalysis research with human\u2013AI\u2013robot collaboration. Nat. Catal. 8, 1135\u20131145 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Langner, S. et al. Beyond ternary OPV: high-throughput experimentation and self-driving laboratories optimize multicomponent systems. Adv. Mater. 32, 1907801 (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Volk, A. A., Campbell, Z. S., Ibrahim, M. Y. S., Bennett, J. A. & Abolhasani, M. Flow chemistry: a sustainable voyage through the chemical universe en route to smart manufacturing. Annu. Rev. Chem. Biomol. Eng. 13, 45\u201372 (2022).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Mukhin, N., Jha, P. & Abolhasani, M. The role of flow chemistry in self-driving labs. Matter 8, 102205 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Granda, J. M., Donina, L., Dragone, V., Long, D.-L. & Cronin, L. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature 559, 377\u2013381 (2018).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hou, W. et al. Automatic generation of 3D-printed reactionware for chemical synthesis digitization using ChemSCAD. ACS Cent. Sci. 7, 212\u2013218 (2021).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Waldron, C. et al. An autonomous microreactor platform for the rapid identification of kinetic models. React. Chem. Eng. 4, 1623\u20131636 (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

McMullen, J. P. & Jensen, K. F. Rapid determination of reaction kinetics with an automated microfluidic system. Org. Process. Res. Dev. 15, 398\u2013407 (2011).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

G\u00f3mez-Bombarelli, R. et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent. Sci. 4, 268\u2013276 (2018).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Shen, R., Luo, G. & Su, A. Bayesian optimization for chemical synthesis in the era of artificial intelligence: advances and applications. Processes 13, 2687 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhou, Z., Li, X. & Zare, R. N. Optimizing chemical reactions with deep reinforcement learning. ACS Cent. Sci. 3, 1337\u20131344 (2017).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Snapp, K. L. et al. Superlative mechanical energy absorbing efficiency discovered through self-driving lab-human partnership. Nat. Commun. 15, 4290 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Canty, R. B. et al. Science acceleration and accessibility with self-driving labs. Nat. Commun. 16, 3856 (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Vescovi, R. et al. Towards a modular architecture for science factories. Digit. Discov. 2, 1980\u20131998 (2023).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Rauschen, R., Guy, M., Hein, J. E. & Cronin, L. Universal chemical programming language for robotic synthesis repeatability. Nat. Synth. 3, 488\u2013496 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Strieth-Kalthoff, F. et al. Delocalized, asynchronous, closed-loop discovery of organic laser emitters. Science 384, eadk9227 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Pendleton, I. M. et al. Experiment specification, capture and laboratory automation technology (ESCALATE): a software pipeline for automated chemical experimentation and data management. MRS Commun. 9, 846\u2013859 (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bai, J. et al. A dynamic knowledge graph approach to distributed self-driving laboratories. Nat. Commun. 15, 462 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Pascazio, L. et al. Chemical species ontology for data integration and knowledge discovery. J. Chem. Inf. Model. 63, 6569\u20136586 (2023).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Walsh, D. J. et al. Community resource for innovation in polymer technology (CRIPT): a scalable polymer material data structure. ACS Cent. Sci. 9, 330\u2013338 (2023).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Guevarra, D. et al. Orchestrating nimble experiments across interconnected labs. Digit. Discov. 2, 1806\u20131812 (2023).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sim, M. et al. ChemOS 2.0: An orchestration architecture for chemical self-driving laboratories. Matter https:\/\/doi.org\/10.1016\/j.matt.2024.04.022<\/a> (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhang, W. et al. IvoryOS: an interoperable web interface for orchestrating Python-based self-driving laboratories. Nat. Commun. 16, 5182 (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Scheffler, M. et al. FAIR data enabling new horizons for materials research. Nature 604, 635\u2013642 (2022).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Adesiji, A. D., Wang, J., Kuo, C.-S. & Brown, K. A. Benchmarking self-driving labs. Digit. Discov. https:\/\/doi.org\/10.1039\/D5DD00337G<\/a> (2026).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Volk, A. A. & Abolhasani, M. Performance metrics to unleash the power of self-driving labs in chemistry and materials science. Nat. Commun. 15, 1378 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kopetz, H. & Steiner, W. in Real-Time Systems: Design Principles for Distributed Embedded Applications (eds Kopetz, H. & Steiner, W.) 325\u2013341 (Springer, 2022); https:\/\/doi.org\/10.1007\/978-3-031-11992-7_13<\/a>.<\/p>\n

Soori, M., Arezoo, B. & Dastres, R. Internet of things for smart factories in industry 4.0, a review. Internet Things Cyber-Phys. Syst. 3, 192\u2013204 (2023).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Remote controlled life sciences lab. Emerald Cloud Lab https:\/\/www.emeraldcloudlab.com\/<\/a> (2026).<\/p>\n

Hogan, A. The semantic web: two decades on. Semantic Web 11, 169\u2013185 (2020).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Shadbolt, N., Berners-Lee, T. & Hall, W. The semantic web revisited. IEEE Intell. Syst. 21, 96\u2013101 (2006).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhang, J. et al. Self-driving AMADAP laboratory: accelerating the discovery and optimization of emerging perovskite photovoltaics. MRS Bull. 49, 1284\u20131294 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ruan, Y. et al. An automatic end-to-end chemical synthesis development platform powered by large language models. Nat. Commun. 15, 10160 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Mahmud, S., Kutlu, M. & Alan, A. T. Optimizing multi-agent system swarm performance through explainable AI. In 2025 11th International Conference on Mechatronics and Robotics Engineering https:\/\/doi.org\/10.1109\/ICMRE64970.2025.10976319<\/a> (IEEE, 2025).<\/p>\n

Tian, F. et al. An outlook on the opportunities and challenges of multi-agent AI systems. Preprint at http:\/\/arxiv.org\/abs\/2505.18397<\/a> (2025).<\/p>\n

Ghafarollahi, A. & Buehler, M. J. SciAgents: automating scientific discovery through bioinspired multi-agent intelligent graph reasoning. Adv. Mater. 37, 2413523 (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Song, T. et al. A multiagent-driven robotic AI chemist enabling autonomous chemical research on demand. J. Am. Chem. Soc. 147, 12534\u201312545 (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Darvish, K. et al. MATTERIX: toward a digital twin for robotics-assisted chemistry laboratory automation. Nat. Comput. Sci. https:\/\/doi.org\/10.1038\/s43588-025-00924-4<\/a> (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Juchli, D. in Smart Biolabs of the Future (eds Beutel, S. & Lenk, F.) 147\u2013174; https:\/\/doi.org\/10.1007\/10_2022_204<\/a> (Springer, 2022).<\/p>\n

Hannelius, T., Salmenpera, M. & Kuikka, S. Roadmap to adopting OPC UA. In 2008 6th IEEE International Conference on Industrial Informatics 756\u2013761 (IEEE, 2008); https:\/\/doi.org\/10.1109\/INDIN.2008.4618203<\/a>.<\/p>\n

Interchangeable Virtual Instruments Foundation. Standards. IVI Foundation https:\/\/ivifoundation.org\/About-IVI\/default.html<\/a> (2001).<\/p>\n

H. M. N. H. Irving, H. Freiser, & T. S. West. Compendium of Analytical Nomenclature (Pergamon, 1978).<\/p>\n

xdl 2.0.1.dev12+g8fdf586b documentation. XDL Documentation https:\/\/croningroup.gitlab.io\/chemputer\/xdl\/<\/a> (2022).<\/p>\n

Autoprotocol. Autoprotocol.org http:\/\/autoprotocol.org\/<\/a> (2023).<\/p>\n

Kearnes, S. M. et al. The open reaction database. J. Am. Chem. Soc. 143, 18820\u201318826 (2021).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Deng, J. et al. ImageNet: a large-scale hierarchical image database. 2009 IEEE Conf. Computer Vis. Pattern Recognit. https:\/\/doi.org\/10.1109\/CVPR.2009.5206848<\/a> (2009).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, A. et al. GLUE: a multi-task benchmark and analysis platform for natural language understanding. Preprint at https:\/\/arxiv.org\/abs\/1804.07461<\/a> (2019).<\/p>\n

Oates, P. M. & Harvey, C. F. A colorimetric reaction to quantify fluid mixing. Exp. Fluids 41, 673\u2013683 (2006).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Plutschack, M. B., Pieber, B., Gilmore, K. & Seeberger, P. H. The hitchhiker\u2019s guide to flow chemistry. Chem. Rev. 117, 11796\u201311893 (2017).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Holland, I. & Davies, J. A. Automation in the life science research laboratory. Front. Bioeng. Biotechnol. 8, 571777 (2020).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Autor, D. H. Why are there still so many jobs? The history and future of workplace automation. J. Econ. Perspect. 29, 3\u201330 (2015).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Dwivedi, Y. K. et al. Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int. J. Inf. Manag. 57, 101994 (2021).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Eshraghian, F., Hafezieh, N., Farivar, F. & de Cesare, S. AI in software programming: understanding emotional responses to GitHub Copilot. Inf. Technol. People 38, 1659\u20131685 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Goethals, F. & Ziegelmayer, J. L. Anxiety buffers and the threat of extreme automation: a terror management theory perspective. Inf. Technol. People 35, 96\u2013118 (2021).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hung, L. et al. Autonomous laboratories for accelerated materials discovery: a community survey and practical insights. Digit. Discov. 3, 1273\u20131279 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Gaidimas, M. A. et al. Computer vision for high-throughput materials synthesis: a tutorial for experimentalists. Digit. Discov. https:\/\/doi.org\/10.1039\/D5DD00384A<\/a> (2026).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sadeghi, S. et al. Engineering a sustainable future: harnessing automation, robotics, and artificial intelligence with self-driving laboratories. ACS Sustain. Chem. Eng. 12, 12695\u201312707 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Nikolaev, P. et al. Autonomy in materials research: a case study in carbon nanotube growth. Npj Comput. Mater. 2, 1\u20136 (2016).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Li, J. et al. Autonomous discovery of optically active chiral inorganic perovskite nanocrystals through an intelligent cloud lab. Nat. Commun. 11, 2046 (2020).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wagner, J. et al. The evolution of materials acceleration platforms: toward the laboratory of the future with AMANDA. J. Mater. Sci. 56, 16422\u201316446 (2021).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Nambiar, A. M. K. et al. Bayesian optimization of computer-proposed multistep synthetic routes on an automated robotic flow platform. ACS Cent. Sci. 8, 825\u2013836 (2022).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Vogler, M. et al. Autonomous battery optimization by deploying distributed experiments and simulations. Adv. Energy Mater. 14, 2403263 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Brocklehurst, C. E. et al. MicroCycle: an integrated and automated platform to accelerate drug discovery. J. Med. Chem. 67, 2118\u20132128 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zaki, M., Prinz, C. & Ruehle, B. A self-driving lab for nano- and advanced materials synthesis. ACS Nano 19, 9029\u20139041 (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hamilton company. Anal. Chem. 47, 1044A\u20131044A (1975).<\/p>\n

Rocha, C., Dias, J., Moreira, A. P., Veiga, G. & Costa, P. A kinesthetic teaching approach for automating micropipetting repetitive tasks. Int. J. Adv. Manuf. Technol. 118, 651\u2013663 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ruzicka, J. & Hansen, E. H. Flow injection analysis with intermittent flow. Patent no. US4315754A (1982).<\/p>\n

HAMILTON. Anal. Chem. 55, 537A (1983).<\/p>\n

About. Laboratory Robotics Interest Group (LRIG) https:\/\/www.lrig.org\/home\/about<\/a>.<\/p>\n

ISLAR\u201998 Sixteenth Annual International Symposium on Laboratory Automation and Robotics Held in Boston. J. Assoc. Lab. Autom. 3, 90\u201391 (1998).<\/p>\n

Hallock, N. Creative combustion: a history of the association for laboratory automation. SLAS Technol. 10, 423\u2013431 (2005).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Thurow, K. & Junginger, S. in Devices and Systems for Laboratory Automation 1\u201339 (John Wiley and Sons, 2022); https:\/\/doi.org\/10.1002\/9783527829446.ch1<\/a>.<\/p>\n

Hahn, H. Introduction to automated chemistry analyzers \u2014 FS3700. OI Analytical Webinars (2020); https:\/\/www.xylem.com\/siteassets\/brand\/oi-analytical\/resources\/presentations\/oia-presentation-automated-chemistry-analyzers-training-fs3700.pdf<\/a>.<\/p>\n

The history of automated liquid handling. LabAutopedia https:\/\/diyhpl.us\/~bryan\/irc\/labautopedia-history-of-automated-liquid-handling.html<\/a> (2009).<\/p>\n

Shimizu, Y. Capacitive liquid level sensor for automatic chemical analyzer. Patent US4818492A (1989).<\/p>\n

From innovation to repetition \u2014 a history of pipetting. Singer Instruments https:\/\/www.singerinstruments.com\/resource\/from-innovation-to-repetition-a-history-of-pipetting\/<\/a> (2026).<\/p>\n

Beugelsdijk, T. J. Association for Laboratory Automation. Wayback Machine https:\/\/web.archive.org\/web\/19961227031521\/http:\/labautomation.org\/<\/a> (1996).<\/p>\n

B\u00e4r, H., Hochstrasser, R. & Papenfu\u00df, B. SiLA: basic standards for rapid integration in laboratory automation. SLAS Technol. 17, 86\u201395 (2012).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Unified Architecture \u2014 Landingpage. OPC Foundation https:\/\/opcfoundation.org\/about\/opc-technologies\/opc-ua\/<\/a> (2026).<\/p>\n

Crook, M. ALA &SBS: two towers of excellence to become one. JALA J. Assoc. Lab. Autom. 15, 9\u20139 (2010).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

SiLA2. GitLab https:\/\/gitlab.com\/SiLA2?sort=created_desc<\/a> (2024).<\/p>\n

Bahr, M. N., Morris, M. A., Tu, N. P. & Nandkeolyar, A. Recent advances in high-throughput automated powder dispensing platforms for pharmaceutical applications. Org. Process. Res. Dev. 24, 2752\u20132761 (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bahr, M. N. et al. Collaborative evaluation of commercially available automated powder dispensing platforms for high-throughput experimentation in pharmaceutical applications. Org. Process. Res. Dev. 22, 1500\u20131508 (2018).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Winicov, H. et al. Chemical process optimization by computer \u2014 a self-directed chemical synthesis system. Anal. Chim. Acta 103, 469\u2013476 (1978).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Di Bella, C. W. & Stevens, W. F. Process optimization by nonlinear programming. Ind. Eng. Chem. Process. Des. Dev. 4, 16\u201320 (1965).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Morgan, S. L. & Deming, S. N. Simplex optimization of analytical chemical methods. Anal. Chem. 46, 1170\u20131181 (1974).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Berridge, J. C. Unattended optimisation of reversed-phase high-performance liquid chromatographic separations using the modified simplex algorithm. J. Chromatogr. A 244, 1\u201314 (1982).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lindsey, J. S., Corkan, L. A., Erb, D. & Powers, G. J. Robotic work station for microscale synthetic chemistry: on-line absorption spectroscopy, quantitative automated thin-layer chromatography, and multiple reactions in parallel. Rev. Sci. Instrum. 59, 940\u2013950 (1988).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kleeman, W. P. & Bailey, L. C. Simplex optimization of the blue tetrazolium assay procedure for \u03b1-ketol steroids. J. Pharm. Sci. 74, 655\u2013659 (1985).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Reiko, M., Mumio, I. & Mitsuru, U. Simplex optimization of reaction condition using laboratory robotic system. \u85e5\u5b78\u96dc\u8a8c 107, 683\u2013689 (1987).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Isenhour, T. L. Robotics in the laboratory. J. Chem. Inf. Comput. Sci. 25, 292\u2013295 (1985).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hoskins, J. C. & Himmelblau, D. M. Artificial neural network models of knowledge representation in chemical engineering. Comput. Chem. Eng. 12, 881\u2013890 (1988).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Jan M. Zytkow, J. Z. Automated Discovery in a Chemistry Laboratory. In AAAI\u201990 Proc. 8th National conference on Artificial Intelligence Vol. 2, 889\u2013894 (AAAI, 1990); https:\/\/dl.acm.org\/doi\/10.5555\/1865609.1865633<\/a>.<\/p>\n

Sasaki, M. A fully automated clinical laboratory: the history of the systemic and robotic automated clinical laboratory at the kochi medical school. Chemom. Intell. Lab. Syst. 21, 159\u2013168 (1993).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lindsey, J. S. & Dixon, J. M. Performance of search algorithms in the examination of chemical reaction spaces with an automated chemistry workstation. JALA J. Assoc. Lab. Autom. 9, 364\u2013374 (2004).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Krishnadasan, S., Brown, R. J. C., deMello, A. J. & deMello, J. C. Intelligent routes to the controlled synthesis of nanoparticles. Lab. Chip 7, 1434\u20131441 (2007).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Segal, M. An operating system for the biology lab. Nature 573, S112\u2013S113 (2019).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Strateos. Strateos Company Fact Sheet https:\/\/go.strateos.com\/hubfs\/Website%20URLs\/Media%20Kit\/Strateos_CompanyFactSheet_Oct2021%20V2.pdf<\/a> (2021).<\/p>\n

Williams, K. et al. Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases. J. R. Soc. Interface 12, 20141289 (2015).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Walter, H., Basset, G., Beierlein, T., Von M\u00fchlenen, A. & Nisato, G. Combinatorial approach for fast screening of functional materials. J. Polym. Sci. Part. B Polym. Phys. 48, 1587\u20131593 (2010).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Trobe, M. & Burke, M. D. The molecular industrial revolution: automated synthesis of small molecules. Angew. Chem. Int. Ed. 57, 4192\u20134214 (2018).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Prabhu, G. R. D. & Urban, P. L. The dawn of unmanned analytical laboratories. TrAC. Trends Anal. Chem. 88, 41\u201352 (2017).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Deneault, J. R. et al. Toward autonomous additive manufacturing: Bayesian optimization on a 3D printer. MRS Bull. 46, 566\u2013575 (2021).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Elder, S. et al. Cross-platform bayesian optimization system for autonomous biological assay development. SLAS Technol. 26, 579\u2013590 (2021).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Rooney, M. B. et al. A self-driving laboratory designed to accelerate the discovery of adhesive materials. Digit. Discov. 1, 382\u2013389 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kanda, G. N. et al. Robotic search for optimal cell culture in regenerative medicine. eLife 11, e77007 (2022).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

MacLeod, B. P. et al. A self-driving laboratory advances the Pareto front for material properties. Nat. Commun. 13, 995 (2022).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ha, T. et al. AI-driven robotic chemist for autonomous synthesis of organic molecules. Sci. Adv. 9, eadj0461 (2023).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lunt, A. M. et al. Modular, multi-robot integration of laboratories: an autonomous workflow for solid-state chemistry. Chem. Sci. 15, 2456\u20132463 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Rapp, J. T., Bremer, B. J. & Romero, P. A. Self-driving laboratories to autonomously navigate the protein fitness landscape. Nat. Chem. Eng. 1, 97\u2013107 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Vogler, M. et al. Brokering between tenants for an international materials acceleration platform. Matter 6, 2647\u20132665 (2023).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Toong, H. D. & Gupta, A. Personal computers. Sci. Am. 247, 86\u2013107 (1982).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Joque, J. The invention of the object: object orientation and the philosophical development of programming languages. Philos. Technol. 29, 335\u2013356 (2016).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Makino, H. Development of the SCARA. J. Robot. Mechatron. 26, 5\u20138 (2014).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

O\u2019Regan, G. A Brief History of Computing (ed. O\u2019Regan, G.) 143\u2013154 (Springer, 2021); https:\/\/doi.org\/10.1007\/978-3-030-66599-9_13<\/a>.<\/p>\n

Introduction to the IETF. IETF https:\/\/www.ietf.org\/about\/introduction\/<\/a> (2014).<\/p>\n

Kogatam, S. The birth, evolution and future of microprocessor. Academia https:\/\/www.academia.edu\/91702498\/The_Birth_Evolution_and_Future_of_Microprocessor<\/a> (2014).<\/p>\n

Pahlavan, K. & Krishnamurthy, P. Evolution and impact of Wi-Fi technology and applications: a historical perspective. Int. J. Wirel. Inf. Netw. 28, 3\u201319 (2021).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Specification Downloads. IVI Foundation https:\/\/ivifoundation.org\/specifications\/default.html<\/a> (2001).<\/p>\n

Colgate, J. E., Wannasuphoprasit, W. & Peshkin, M. A. in ASME 1996 International Mechanical Engineering Congress and Exposition 433\u2013439 (ASME, 2023); https:\/\/doi.org\/10.1115\/IMECE1996-0367<\/a>.<\/p>\n

Voelz, V. A., Pande, V. S. & Bowman, G. R. Folding@home: achievements from over 20 years of citizen science herald the exascale era. Biophys. J. 122, 2852\u20132863 (2023).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Collobert, R. Bengio, S. & Mari\u00e9thoz, J. Torch: A Modular Machine Learning Software Library https:\/\/infoscience.epfl.ch\/bitstreams\/7513f344-91b6-427d-a020-7836b150a150\/download<\/a> (2002).<\/p>\n

Guti\u00e9rrez, J. A. On the Use of IEEE Std. 802.15.4 to enable wireless sensor networks in building automation. Int. J. Wirel. Inf. Netw. 14, 295\u2013301 (2007).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Goldsborough, P. A tour of TensorFlow. Preprint at https:\/\/arxiv.org\/abs\/1610.01178<\/a> (2016).<\/p>\n

Ketkar, N. in Deep Learning with Python: A Hands-on Introduction (ed. Ketkar, N.) 97\u2013111 (Apress, 2017); https:\/\/doi.org\/10.1007\/978-1-4842-2766-4_7<\/a>.<\/p>\n

Jia, Y. et al. Caffe: convolutional architecture for fast feature embedding. In Proc. 22nd ACM International Conference on Multimedia 675\u2013678 (Association for Computing Machinery, 2014); https:\/\/doi.org\/10.1145\/2647868.2654889<\/a>.<\/p>\n

onnx\/onnx. Open Neural Network Exchange (GitHub, 2026); https:\/\/github.com\/onnx\/onnx<\/a>.<\/p>\n

Unwala, I., Taqvi, Z. & Lu, J. Thread: An IoT protocol. In 2018 IEEE Green Technologies Conference (GreenTech); https:\/\/doi.org\/10.1109\/GreenTech.2018.00037<\/a> (IEEE, 2018).<\/p>\n

Armoni, M. Tensor Processing Units (TPU): A Technical Analysis and Their Impact on Artificial Intelligence https:\/\/tech4future.info\/wp-content\/uploads\/2024\/11\/Tensor-Processing-Units-TPU-Paper-ENG.pdf<\/a> (2024).<\/p>\n","protected":false},"excerpt":{"rendered":"Kalinin, S. V. et al. Automated and autonomous experiments in electron and scanning probe microscopy. ACS Nano 15,…\n","protected":false},"author":2,"featured_media":552239,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[97867,40915,39810,5738,97795,2294,269437,45950,111,139,69,78413,65009,145],"class_list":["post-552238","post","type-post","status-publish","format-standard","has-post-thumbnail","category-technology","tag-analytical-chemistry","tag-biochemistry","tag-catalysis","tag-chemical-engineering","tag-chemistry-food-science","tag-general","tag-inorganic-chemistry","tag-materials-for-energy-and-catalysis","tag-new-zealand","tag-newzealand","tag-nz","tag-organic-chemistry","tag-physical-chemistry","tag-technology"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/posts\/552238","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/comments?post=552238"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/posts\/552238\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/media\/552239"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/media?parent=552238"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/categories?post=552238"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/tags?post=552238"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}