A curated list of self-driving laboratories (SDLs) that combine hardware automation and artificial intelligence to accelerate scientific discovery.

• Review Papers
• SDL Examples
  • Emoji Key
  • Academic Research
  • Education
  • Industry
  • Prospective
• Software
  • Experimental Orchestration
  • Optimization
  • Other
• People
• Media
• Contribute
• License

A BibTeX file of all the references below with a DOI can be downloaded here

Review papers for self-driving laboratories, sorted by publication date.

• Role of AI in Experimental Materials Science. Abolhasani, M.; Brown, K. A.; Guest Editors. MRS Bulletin 2023.
• Next-Generation Intelligent Laboratories for Materials Design and Manufacturing. Peng, X.; Wang, X.; Brown, K. A.; Abolhasani, M. MRS Bulletin 2023.
• Toward Autonomous Laboratories: Convergence of Artificial Intelligence and Experimental Automation Xie, Y.; Sattari, K.; Zhang, C.; Lin, J. Progress in Materials Science 2023, 132, 101043.
• The Rise of Self-Driving Labs in Chemical and Materials Sciences. Abolhasani, M.; Kumacheva, E. Nat. Synth 2023, 1–10.

• Research Acceleration in Self‐Driving Labs: Technological Roadmap toward Accelerated Materials and Molecular Discovery. Delgado-Licona, F.; Abolhasani, M. Advanced Intelligent Systems 2022, 2200331.
• Artificial Intelligence for Materials Research at Extremes. Maruyama, B.; Hattrick-Simpers, J.; Musinski, W.; Graham-Brady, L.; Li, K.; Hollenbach, J.; Singh, A.; Taheri, M. L. MRS Bulletin 2022, 47 (11), 1154–1164.
• Linking Scientific Instruments and Computation: Patterns, Technologies, and Experiences. Vescovi, R.; Chard, R.; Saint, N. D.; Blaiszik, B.; Pruyne, J.; Bicer, T.; Lavens, A.; Liu, Z.; Papka, M. E.; Narayanan, S.; Schwarz, N.; Chard, K.; Foster, I. T. Patterns 2022, 3 (10), 100606.
• Autonomous (AI-Driven) Materials Science. Green, M. L.; Maruyama, B.; Schrier, J. Applied Physics Reviews 2022, 9 (3), 030401.
• Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab. Seifrid, M.; Pollice, R.; Aguilar-Granda, A.; Morgan Chan, Z.; Hotta, K.; Ser, C. T.; Vestfrid, J.; Wu, T. C.; Aspuru-Guzik, A. Acc. Chem. Res. 2022, acs.accounts.2c00220.
• Cloud Labs: Where Robots Do the Research. Arnold, C. Nature 2022, 606 (7914), 612–613.
• Reaching Critical MASS: Crowdsourcing Designs for the next Generation of Materials Acceleration Platforms. Seifrid, M.; Hattrick-Simpers, J.; Aspuru-Guzik, A.; Kalil, T.; Cranford, S. Matter 2022, 5 (7), 1972–1976.
• Defining Levels of Automated Chemical Design. Goldman, B.; Kearnes, S.; Kramer, T.; Riley, P.; Walters, W. P. J. Med. Chem. 2022, 65 (10), 7073–7087.
• Toward Autonomous Materials Research: Recent Progress and Future Challenges. Montoya, J. H.; Aykol, M.; Anapolsky, A.; Gopal, C. B.; Herring, P. K.; Hummelshøj, J. S.; Hung, L.; Kwon, H.-K.; Schweigert, D.; Sun, S.; Suram, S. K.; Torrisi, S. B.; Trewartha, A.; Storey, B. D. Applied Physics Reviews 2022, 9 (1), 011405.

• Enabling Modular Autonomous Feedback-Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration. Rahmanian, F.; Flowers, J.; Guevarra, D.; Richter, M.; Fichtner, M.; Donnely, P.; Gregoire, J. M.; Stein, H. S. Advanced Materials Interfaces 2022, 9 (8), 2101987.
• Flexible Automation Accelerates Materials Discovery. MacLeod, B. P.; Parlane, F. G. L.; Brown, A. K.; Hein, J. E.; Berlinguette, C. P. Nat. Mater. 2021.
• Autonomous Experimentation Systems for Materials Development: A Community Perspective. Stach, E.; DeCost, B.; Kusne, A. G.; Hattrick-Simpers, J.; Brown, K. A.; Reyes, K. G.; Schrier, J.; Billinge, S.; Buonassisi, T.; Foster, I.; Gomes, C. P.; Gregoire, J. M.; Mehta, A.; Montoya, J.; Olivetti, E.; Park, C.; Rotenberg, E.; Saikin, S. K.; Smullin, S.; Stanev, V.; Maruyama, B. Matter 2021, 4 (9), 2702–2726.
• The Role of Machine Learning Algorithms in Materials Science: A State of Art Review on Industry 4.0. Choudhury, A. Arch Computat Methods Eng 2021, 28 (5), 3361–3381.

• Autonomous Discovery in the Chemical Sciences Part II: Outlook. Coley, C. W.; Eyke, N. S.; Jensen, K. F. Angew. Chem. Int. Ed. 2020, 59 (52), 23414–23436.
• Autonomous Discovery in the Chemical Sciences Part I: Progress. Coley, C. W.; Eyke, N. S.; Jensen, K. F. Angew. Chem. Int. Ed. 2020, 59 (51), 22858–22893.
• Materials Acceleration Platforms: On the Way to Autonomous Experimentation. Flores-Leonar, M. M.; Mejía-Mendoza, L. M.; Aguilar-Granda, A.; Sanchez-Lengeling, B.; Tribukait, H.; Amador-Bedolla, C.; Aspuru-Guzik, A. Current Opinion in Green and Sustainable Chemistry 2020, 25, 100370.

• A DIY Approach to Automating Your Lab. May, M. Nature 2019, 569 (7757), 587–588.

• The Internet of Things Comes to the Lab. Perkel, J. M. Nature 2017, 542 (7639), 125–126.

Examples of SDLs for academic research, education, and industry.

The following emoji are used to help represent fully autonomous vs. manual intervention for various categories.

Examples of SDLs which are used primarily in academic research settings.

• 🧪🔬🏗️💻 | Autonomous, multi-property-driven molecular discovery: From predictions to measurements and back. Koscher, B.; Canty, R. B.; McDonald, M. A.; Greenman, K. P.; McGill, C. J.; Bilodeau, C. L.; Jin, W.; Wu, H.; Vermeire, F. H.; Jin, B.; Hart, T.; Kulesza, T.; Li, S.-C.; Jaakkola, T. S.; Barzilay, R.; Gómez-Bombarelli, R.; Green, W. H.; & Jensen, K. F. ChemRxiv 2023, doi:10.26434/chemrxiv-2023-r7b01.
• 🧪🔬🏗️💻 | Self-driving laboratories to autonomously navigate the protein fitness landscape. Rapp, J. T.; Bremer, B. J.; Romero, P. A. bioRxiv 2023, doi:10.1101/2023.05.20.541582.
• 🧪🔬🏗️💻 | NIMS-OS: An automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science. Tamura, R.; Tsuda, K.; Matsuda, S. arXiv 2023, doi:10.48550/arXiv.2304.13927.

• 🧪🔬🏗️💻 | A Self-Driving Laboratory Designed to Accelerate the Discovery of Adhesive Materials. Rooney, M. B.; MacLeod, B. P.; Oldford, R.; Thompson, Z. J.; White, K. L.; Tungjunyatham, J.; Stankiewicz, B. J.; Berlinguette, C. P. Digital Discovery 2022, 10.1039.D2DD00029F.
• 🧪🔬✖️💻 | Autonomous retrosynthesis of gold nanoparticles via spectral shape matching. Vaddi, Kiran; Huat Thart Chiang; and Lilo D. Pozzo. Digital Discovery 2022, 10.1039/D2DD00025C.
• 🧪🔬🏗️💻 | Physics Discovery in Nanoplasmonic Systems via Autonomous Experiments in Scanning Transmission Electron Microscopy. Roccapriore, K. M., Kalinin, S. V., Ziatdinov, M. Adv. Sci. 2022, 9, 2203422.

• 🧪🔬🏗️💻 | Autonomous Materials Synthesis via Hierarchical Active Learning of Nonequilibrium Phase Diagrams. Ament, S.; Amsler, M.; Sutherland, D. R.; Chang, M.-C.; Guevarra, D.; Connolly, A. B.; Gregoire, J. M.; Thompson, M. O.; Gomes, C. P.; van Dover, R. B. Sci. Adv. 2021, 7 (51), eabg4930.
• 🧪🔬🏗️💻 | Accelerate Synthesis of Metal–Organic Frameworks by a Robotic Platform and Bayesian Optimization. Xie, Y.; Zhang, C.; Deng, H.; Zheng, B.; Su, J.-W.; Shutt, K.; Lin, J. ACS Appl. Mater. Interfaces 2021, 13 (45), 53485–53491.
• 🧪🔬🏗️💻 | Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy. Kalinin, S. V.; Ziatdinov, M.; Hinkle, J.; Jesse, S.; Ghosh, A.; Kelley, K. P.; Lupini, A. R.; Sumpter, B. G.; Vasudevan, R. K. ACS Nano 2021, 15 (8), 12604–12627.
• 🧪🔬✖️💻 | Realization of closed-loop optimization of epitaxial titanium nitride thin-film growth via machine learning. Ohkubo, I.; Hou, Z.; Lee, J. N.; Aizawa, T.' Lippmaa, M.; Chikyow, T.; Mori, T. Materials Today Physics 2021, 16, 100296.
• 🧪🔬🏗️💻 | Toward Autonomous Additive Manufacturing: Bayesian Optimization on a 3D Printer. Deneault, J. R.; Chang, J.; Myung, J.; Hooper, D.; Armstrong, A.; Pitt, M.; Maruyama, B. MRS Bulletin 2021, 46 (7), 566–575.

• 🧪🔬🏗️💻 | Autonomous Discovery of Battery Electrolytes with Robotic Experimentation and Machine Learning. Dave, A.; Mitchell, J.; Kandasamy, K.; Wang, H.; Burke, S.; Paria, B.; Póczos, B.; Whitacre, J.; Viswanathan, V. Cell Reports Physical Science 2020, 1 (12), 100264.
• 🧪🔬🏗️💻 | Self-Driving Laboratory for Accelerated Discovery of Thin-Film Materials. MacLeod, B. P.; Parlane, F. G. L.; Morrissey, T. D.; Häse, F.; Roch, L. M.; Dettelbach, K. E.; Moreira, R.; Yunker, L. P. E.; Rooney, M. B.; Deeth, J. R.; Lai, V.; Ng, G. J.; Situ, H.; Zhang, R. H.; Elliott, M. S.; Haley, T. H.; Dvorak, D. J.; Aspuru-Guzik, A.; Hein, J. E.; Berlinguette, C. P. Sci. Adv. 2020, 6 (20), eaaz8867.
• 🧪🔬🏗️💻 | ChemOS: An Orchestration Software to Democratize Autonomous Discovery. Roch, L. M.; Häse, F.; Kreisbeck, C.; Tamayo-Mendoza, T.; Yunker, L. P. E.; Hein, J. E.; Aspuru-Guzik, A. PLoS ONE 2020, 15 (4), e0229862.
• 🧪🔬🏗️💻 | Beyond Ternary OPV: High‐Throughput Experimentation and Self‐Driving Laboratories Optimize Multicomponent Systems. Langner, S.; Häse, F.; Perea, J. D.; Stubhan, T.; Hauch, J.; Roch, L. M.; Heumueller, T.; Aspuru‐Guzik, A.; Brabec, C. J. Adv. Mater. 2020, 32 (14), 1907801.
• 🧪🔬🏗️💻 | Autonomous materials synthesis by machine learning and robotics. Shimizu, R.; Kobayashi, S.; Watanabe, Y.; Ando, Y.; Hitosugi, T. APL Mater. 2020, 8 (11), 111110.

• 🧪🔬🏗️💻 | Networking Chemical Robots for Reaction Multitasking. Caramelli, D.; Salley, D.; Henson, A.; Camarasa, G. A.; Sharabi, S.; Keenan, G.; Cronin, L. Nat Commun 2018, 9 (1), 3406.

• 🧪🔬🏗️💻 | Autonomy in Materials Research: A Case Study in Carbon Nanotube Growth. Nikolaev, P.; Hooper, D.; Webber, F.; Rao, R.; Decker, K.; Krein, M.; Poleski, J.; Barto, R.; Maruyama, B. npj Comput Mater 2016, 2 (1), 16031.

• 🧪🔬🏗️💻 | Evolution of Oil Droplets in a Chemorobotic Platform. Gutierrez, J. M. P.; Hinkley, T.; Taylor, J. W.; Yanev, K.; Cronin, L. Nat Commun 2014, 5 (1), 5571.

Examples of SDLs which are used primarily in educational settings.

• 🧪🔬🏗️💻 | Automated PH Adjustment Driven by Robotic Workflows and Active Machine Learning. Pomberger, A.; Jose, N.; Walz, D.; Meissner, J.; Holze, C.; Kopczynski, M.; Müller-Bischof, P.; Lapkin, A. A. Chemical Engineering Journal 2023, 451, 139099.
• Build Instructions for Closed-Loop Spectroscopy Lab: Light-Mixing Demo. Baird, S. G.; Sparks, T. D. ChemRxiv January 9, 2023.

• 🧪🔬🏗️💻 | What Is a Minimal Working Example for a Self-Driving Laboratory?. Baird, S. G.; Sparks, T. D. Matter 2022, 5 (12), 4170–4178.
• 🧪🔬🏗️💻 | The LEGOLAS Kit: A Low-Cost Robot Science Kit for Education with Symbolic Regression for Hypothesis Discovery and Validation. Saar, L.; Liang, H.; Wang, A.; McDannald, A.; Rodriguez, E.; Takeuchi, I.; Kusne, A. G. MRS Bulletin 2022, 47 (9), 881–885.

• 🧪🔬🏗️💻 | Augmented Titration Setup for Future Teaching Laboratories. Yang, F.; Lai, V.; Legard, K.; Kozdras, S.; Prieto, P. L.; Grunert, S.; Hein, J. E. J. Chem. Educ. 2021, 98 (3), 876–881.

• 🧪🔬🏗️💻 | ChemOS: An Orchestration Software to Democratize Autonomous Discovery. Roch, L. M.; Häse, F.; Kreisbeck, C.; Tamayo-Mendoza, T.; Yunker, L. P. E.; Hein, J. E.; Aspuru-Guzik, A. PLoS ONE 2020, 15 (4), e0229862.
• 🧪🔬🏗️💻 | Autonomous Titration for Chemistry Classrooms: Preparing Students for Digitized Chemistry Laboratories. Häse, F.; Tamayo-Mendoza, T.; Boixo, C.; Romero, J.; Roch, L.; Aspuru-Guzik, A. ChemRxiv 2020.

• 🧪🔬🏗️💻 | Rethinking a Timeless Titration Experimental Setup through Automation and Open-Source Robotic Technology: Making Titration Accessible for Students of All Abilities. Soong, R.; Agmata, K.; Doyle, T.; Jenne, A.; Adamo, A.; Simpson, A. J. J. Chem. Educ. 2019, 96 (7), 1497–1501.

Industry examples involving SDLs.

• IBM RoboRXN
• Emerald Cloud Lab
• Strateos
• Culture Biosciences
• Arctoris
• Kebotix
• CMU Cloud Lab
• Argonne National Laboratory

• Atinary
• IBM Accelerated Discovery
• Citrine Informatics
• Sunthetics
• Globus

Ideas for SDLs.

• Reproducible Sorbent Materials Foundry for Carbon Capture at Scale. McDannald, A.; Joress, H.; DeCost, B.; Baumann, A. E.; Kusne, A. G.; Choudhary, K.; Yildirim, T.; Siderius, D. W.; Wong-Ng, W.; Allen, A. J.; Stafford, C. M.; Ortiz-Montalvo, D. L. CR-PHYS-SC 2022, 3 (10).
• An Object-Oriented Framework to Enable Workflow Evolution across Materials Acceleration Platforms. Leong, C. J.; Low, K. Y. A.; Recatala-Gomez, J.; Quijano Velasco, P.; Vissol-Gaudin, E.; Tan, J. D.; Ramalingam, B.; I Made, R.; Pethe, S. D.; Sebastian, S.; Lim, Y.-F.; Khoo, Z. H. J.; Bai, Y.; Cheng, J. J. W.; Hippalgaonkar, K. Matter 2022, 5 (10), 3124–3134.
• Designing Workflows for Materials Characterization. Kalinin, S. V., Ziatdinov, M., Ahmadi, M., Ghosh, A., Roccapriore, K., Liu, Y., & Vasudevan, R. K. (2023). arXiv:2302.04397.

Examples of experimental orchestration, optimization, and other software.

Experimental orchestration software for autonomously controlling software-hardware communication. See also @sgbaird's lab-automation list.

• Alab Management [code] [docs]
• Bluesky [code] [docs]
• HELAO [code] [paper]
• Chemios [code]
• ARES OS [code] [paper]
• PLACE [code] [paper]
• XDL [code] [docs] [paper]
• self-driving-lab-demo [code] [docs]
• NIMS-OS [code] [docs] [paper]

Open-source and proprietary optimization software for iteratively suggesting next experiments (i.e., adaptive experimentation).

• Adaptive Experimentation Platform (Ax) is a user-friendly, modular, and actively developed general-purpose Bayesian optimization platform with support for simple and advanced optimization tasks such as noisy, multi-objective, multi-task, multi-fidelity, batch, high-dimensional, linearly constrained, nonlinearly constrained, mixed continuous/discrete/categorical, and contextual Bayesian optimization.
• BoTorch is the backbone that makes up the Ax platform and allows for greater customization and specialized algorithms such as risk-averse Bayesian optimization and constraint active search.
• Dragonfly is an open source python library for scalable Bayesian optimization with multi-objective and multi-fidelity support.
• RayTune offers experiment execution and hyperparameter tuning at any scale with many supported search algorithms and trial schedulers under a common interface.
• Aspuru-Guzik Group
  • Atlas is a Python package that offers Bayesian optimization tailored towards real-world experimental science problems: mixed parameters, multi-objective, noisy, constrained, multi-fidelity, and meta-learning optimization along with search space expansion/contraction. [WIP]
  • Chimera is a hierarchy-based multi-objective optimization scalarizing function.
  • Gryffin enables Bayesian optimization of continuous and categorical variables with support for physicochemical descriptors and batch optimization.
  • Gemini is a scalable multi-fidelity Bayesian optimization technique and is supported by Gryffin.
  • Golem is an algorithm that helps identify optimal solutions that are robust to input uncertainty (i.e., robust optimization).
  • Phoenics is a linear-scaling Bayesian optimization algorithm with support for batch and periodic parameter optimization.
• BoFire is a Bayesian Optimization Framework Intended for Real Experiments (under development) with support for advanced optimization tasks such as mixed variables, multiple objectives, and generic constraints.
• NIMS-OS is a Python package (+GUI) for workflow orchestration and multi-objective optimization software that supports BLOX, PDC, random exploration, and a multi-objective variant of PHYSBO.
• Summit is a set of tools for optimising chemical processes with a wide variety of design of experiments (DoE) and adaptive design methods along with benchmarks.
• GPax is a small Python package for physics-based Gaussian processes (GPs) built on top of NumPyro and JAX that take advantage of prior physical knowledge and different data modalities for active learning and Bayesian optimization.

• ChemOS
• The Citrine Platform
• IBM Accelerated Discovery
  • RXN for Chemistry
  • Simulation Toolkit For Scientific Discovery (ST4SD)
  • Generative Toolkit for Scientific Discovery
  • Deep Search (semantic extraction from documents)

• Benchmarking
  • Olympus is a benchmarking framework based primarily on data collected from experimental self-driving lab setups.
• Functional Data Analysis
  • Amplitude-Phase-Distance is a Riemannian differential geometry toolbox to compute a `shape' distance between electromagnetic spectra, scattering, or diffraction profiles.
  • autophasemap is a clustering and phase mapping toolbox using the amplitude phase distance for autonomous construction of phasemaps from spectra-like data.

WIP

https://acceleration.utoronto.ca/researcher

• Self-driving Laboratories do Research on Autopilot. Hackaday 2022.
• Lowe, D. The Downside of Chemistry Automation. (accessed 2022-08-26).

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