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https://github.com/AccelerationConsortium/awesome-self-driving-labs

A curated list of self-driving laboratories that combine hardware automation and artificial intelligence to accelerate scientific discovery.
https://github.com/AccelerationConsortium/awesome-self-driving-labs

List: awesome-self-driving-labs

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

Awesome Lists containing this project

README 

        
# Awesome Self-Driving Labs [![Awesome](https://awesome.re/badge.svg)](https://awesome.re) [![DOI](https://zenodo.org/badge/614559484.svg)](https://zenodo.org/badge/latestdoi/614559484) 



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



## Contents 



- [Review Papers](#review-papers)

- [SDL Examples](#sdl-examples)

  - [Emoji Key](#emoji-key)

  - [Academic Research](#academic-research)

  - [Education](#education)

  - [Industry](#industry)

  - [Prospective](#prospective)

- [Software](#software)

  - [Workflow Orchestration](#workflow-orchestration)

  - [Optimization](#optimization)

  - [Research Data Management](#research-data-management)

  - [Other](#other)

- [Hardware](#hardware)

- [People](#people)

- [Media](#media)

- [Contribute](#contribute)

- [License](#license)



> A `BibTeX` file of all the references below with a `DOI` can be downloaded [here](bibtex/references.bib)



## Review Papers



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



### 2023 

- [Role of AI in Experimental Materials Science](https://doi.org/10.1557/s43577-023-00482-y). Abolhasani, M.; Brown, K. A.; Guest Editors. *MRS Bulletin* 2023.

- [Next-Generation Intelligent Laboratories for Materials Design and Manufacturing](https://doi.org/10.1557/s43577-023-00481-z). Peng, X.; Wang, X.; Brown, K. A.; Abolhasani, M. *MRS Bulletin* 2023.

- [Toward Autonomous Laboratories: Convergence of Artificial Intelligence and Experimental Automation](https://doi.org/10.1016/j.pmatsci.2022.101043) 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](https://doi.org/10.1038/s44160-022-00231-0). Abolhasani, M.; Kumacheva, E. *Nat. Synth* 2023, 1–10.

- [The Digital Lab Framework as part of The World Avatar](https://como.ceb.cam.ac.uk/preprints/314/). Rihm, S. D.; Bai, J.; Kondinski, A.; Mosbach, S.; Akroyd, J.; Kraft, M. *preprint* 2023.



### 2022 

- [Research Acceleration in Self‐Driving Labs: Technological Roadmap toward Accelerated Materials and Molecular Discovery](https://doi.org/10.1002/aisy.202200331). Delgado-Licona, F.; Abolhasani, M. *Advanced Intelligent Systems* 2022, 2200331.

- [Artificial Intelligence for Materials Research at Extremes](https://doi.org/10.1557/s43577-022-00466-4). 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](https://doi.org/10.1016/j.patter.2022.100606). 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](https://doi.org/10.1063/5.0118872). 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](https://doi.org/10.1021/acs.accounts.2c00220). 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](https://doi.org/10.1038/d41586-022-01618-x). Arnold, C. *Nature* 2022, 606 (7914), 612–613.

- [Reaching Critical MASS: Crowdsourcing Designs for the next Generation of Materials Acceleration Platforms](https://doi.org/10.1016/j.matt.2022.05.035). Seifrid, M.; Hattrick-Simpers, J.; Aspuru-Guzik, A.; Kalil, T.; Cranford, S. *Matter* 2022, 5 (7), 1972–1976.

- [Defining Levels of Automated Chemical Design](https://doi.org/10.1021/acs.jmedchem.2c00334). 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](https://doi.org/10.1063/5.0076324). 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.

- [From Platform to Knowledge Graph: Evolution of Laboratory Automation](https://doi.org/10.1021/jacsau.1c00438). Bai, J.; Cao, L.; Mosbach, S.; Akroyd, J.; Lapkin, A. A.; Kraft, K. *JACS Au* 2022, 2 (2), 292–309.



### 2021 

- [Enabling Modular Autonomous Feedback-Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration](https://doi.org/10.1002/admi.202101987). 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](https://doi.org/10.1038/s41563-021-01156-3). 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](https://doi.org/10.1016/j.matt.2021.06.036). 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](https://doi.org/10.1007/s11831-020-09503-4). Choudhury, A. *Arch Computat Methods Eng* 2021, 28 (5), 3361–3381.



### 2020 

- [Autonomous Discovery in the Chemical Sciences Part II: Outlook](https://doi.org/10.1002/anie.201909989). 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](https://doi.org/10.1002/anie.201909987). 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](https://doi.org/10.1016/j.cogsc.2020.100370). 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.



### 2019 

- [A DIY Approach to Automating Your Lab](https://doi.org/10.1038/d41586-019-01590-z). May, M. *Nature* 2019, 569 (7757), 587–588.



### 2017 

- [The Internet of Things Comes to the Lab](https://doi.org/10.1038/542125a). Perkel, J. M. *Nature* 2017, 542 (7639), 125–126.



## SDL Examples



Examples of SDLs for [academic research](#academic-research), [education](#education), and [industry](#industry).



### Emoji Key

The following emoji are used to help [represent full autonomy vs. manual intervention](https://github.com/sgbaird/awesome-self-driving-labs/discussions/15) for various categories.

| Category | Emoji |

| ---- | ---- |

| Synthesis |🧪|

| Characterization | 🔬|

| Sample transfer |🏗️|

| Experiment planning |💻|

| Manual intervention |✖️|



### Academic Research



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



#### 2024 

- 🧪🔬🏗️💻 | [A dynamic knowledge graph approach to distributed self-driving laboratories](https://doi.org/10.1038/s41467-023-44599-9). Bai, J.; Mosbach, S.; Taylor, C. J.; Karan, D.; Lee, K. F.; Rihm, S. D.; Akroyd, J.; Lapkin, A. A.; Kraft, M. *Nat. Commun.* 2024, 15, 462.



#### 2023 

- 🧪🔬🏗️✖️ | [Powder-Bot: A Modular Autonomous Multi-Robot Workflow for Powder X-Ray Diffraction](https://doi.org/10.48550/arXiv.2309.00544). Lunt, A. M.; Fakhruldeen, H.; Pizzuto, G.; Longley, L.; White, A.; Rankin, N.; Clowes, R.; Alston, B. M.; Cooper, A. I.; Chong, S. Y. *arXiv* 2023.

- 🧪🔬🏗️💻 | [A Robotic Platform for the Synthesis of Colloidal Nanocrystals](https://doi.org/10.1038/s44160-023-00250-5). Zhao, H.; Chen, W.; Huang, H.; Sun, Z.; Chen, Z.; Wu, L.; Zhang, B.; Lai, F.; Wang, Z.; Adam, M. L.; Pang, C. H.; Chu, P. K.; Lu, Y.; Wu, T.; Jiang, J.; Yin, Z.; Yu, X.-F. *Nat. Synth* 2023.

- 🧪🔬🏗️💻 | [Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back](https://doi.org/10.1126/science.adi1407). 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. *Science* 2023.

- 🧪🔬🏗️💻 | [Self-driving laboratories to autonomously navigate the protein fitness landscape](https://doi.org/10.1101/2023.05.20.541582). Rapp, J. T.; Bremer, B. J.; Romero, P. A. *bioRxiv* 2023.

- 🧪🔬🏗️💻 | [NIMS-OS: An automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science](https://doi.org/10.48550/arXiv.2304.13927). Tamura, R.; Tsuda, K.; Matsuda, S. *arXiv* 2023.



#### 2022 

- 🧪🔬🏗️💻 | [A Self-Driving Laboratory Designed to Accelerate the Discovery of Adhesive Materials](https://doi.org/10.1039/D2DD00029F). 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.

- 🧪🔬🏗️💻 | [A self-driving laboratory advances the Pareto front for material properties](https://doi.org/10.1038/s41467-022-28580-6). MacLeod, B. P., Parlane, F. G. L., Rupnow, C. C., Dettelbach, K. E., Elliott, M. S., Morrissey, T. D., Haley, T. H., Proskurin, O., Rooney, M. B., Taherimakhsousi, N., Dvorak, D. J., Chiu, H. N., Waizenegger, C. E. B., Ocean, K., Mokhtari, M. & Berlinguette, C. P. *Nat Commun.* 2022, 13, 995.

- 🧪🔬✖️💻 | [Autonomous retrosynthesis of gold nanoparticles via spectral shape matching](https://doi.org/10.1039/D2DD00025C). 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](https://doi.org/10.1002/advs.202203422). Roccapriore, K. M., Kalinin, S. V., Ziatdinov, M. *Adv. Sci.* 2022, 9, 2203422. 



#### 2021 

- 🧪🔬🏗️💻 | [Autonomous Materials Synthesis via Hierarchical Active Learning of Nonequilibrium Phase Diagrams](https://doi.org/10.1126/sciadv.abg4930). 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](https://doi.org/10.1021/acsami.1c16506). 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](https://doi.org/10.1021/acsnano.1c02104). 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](https://doi.org/10.1016/j.mtphys.2020.100296). 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](https://doi.org/10.1557/s43577-021-00051-1). Deneault, J. R.; Chang, J.; Myung, J.; Hooper, D.; Armstrong, A.; Pitt, M.; Maruyama, B. *MRS Bulletin* 2021, 46 (7), 566–575.

- 🧪🔬🏗️💻 | [Using simulation to accelerate autonomous experimentation: A case study using mechanics](https://doi.org/10.1016/j.isci.2021.102262). Gongora, A. E.; Snapp, K. L.; Whiting, E.; Riley, P.; Reyes, K. G.; Morgan, E. F.; Brown, K. A., *Iscience* 2021, 24(4).



#### 2020 

- 🧪🔬🏗️💻 | [Autonomous Discovery of Battery Electrolytes with Robotic Experimentation and Machine Learning](https://doi.org/10.1016/j.xcrp.2020.100264). 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](https://doi.org/10.1126/sciadv.aaz8867). 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](https://doi.org/10.1371/journal.pone.0229862). 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](https://doi.org/10.1002/adma.201907801). 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](https://doi.org/10.1063/5.0020370). Shimizu, R.; Kobayashi, S.; Watanabe, Y.; Ando, Y.; Hitosugi, T. *APL Mater.* 2020, 8 (11), 111110.

- 🧪🔬🏗️💻 | [A Bayesian experimental autonomous researcher for mechanical design](https://doi.org/10.1126/sciadv.aaz1708). Gongora, A. E.; Xu, B.; Perry, W.; Okoye, C.; Riley, P.; Reyes, K. G.; Morgan, E. F.; Brown, K. A. *Sci. Adv.* 2020, 6 (15), eaaz1708.



#### 2018 

- 🧪🔬🏗️💻 | [Networking Chemical Robots for Reaction Multitasking](https://doi.org/10.1038/s41467-018-05828-8). Caramelli, D.; Salley, D.; Henson, A.; Camarasa, G. A.; Sharabi, S.; Keenan, G.; Cronin, L. *Nat Commun* 2018, 9 (1), 3406.



#### 2016 

- 🧪🔬🏗️💻 | [Autonomy in Materials Research: A Case Study in Carbon Nanotube Growth](https://doi.org/10.1038/npjcompumats.2016.31). 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.



#### 2014 

- 🧪🔬🏗️💻 | [Evolution of Oil Droplets in a Chemorobotic Platform](https://doi.org/10.1038/ncomms6571). Gutierrez, J. M. P.; Hinkley, T.; Taylor, J. W.; Yanev, K.; Cronin, L. *Nat Commun* 2014, 5 (1), 5571.



### Education



Examples of SDLs which are used primarily in educational settings.



#### 2023 

- 🧪🔬🏗️💻 | [Automated PH Adjustment Driven by Robotic Workflows and Active Machine Learning](https://doi.org/10.1016/j.cej.2022.139099). 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](https://doi.org/10.26434/chemrxiv-2023-xgx5h). Baird, S. G.; Sparks, T. D. *ChemRxiv* January 9, 2023.

- 🧪🔬🏗️💻 | [Driving school for self-driving labs](https://doi.org/10.1039/D3DD00150D). Snapp, K. L.; Brown, K. A. *Digital Discovery* 2023, 10.1039/D3DD00150D.



#### 2022 

- 🧪🔬🏗️💻 | [What Is a Minimal Working Example for a Self-Driving Laboratory?](https://doi.org/10.1016/j.matt.2022.11.007). 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](https://doi.org/10.1557/s43577-022-00430-2). Saar, L.; Liang, H.; Wang, A.; McDannald, A.; Rodriguez, E.; Takeuchi, I.; Kusne, A. G. *MRS Bulletin* 2022, 47 (9), 881–885.



#### 2021 

- 🧪🔬🏗️💻 | [Augmented Titration Setup for Future Teaching Laboratories](https://doi.org/10.1021/acs.jchemed.0c01394). Yang, F.; Lai, V.; Legard, K.; Kozdras, S.; Prieto, P. L.; Grunert, S.; Hein, J. E. J. *Chem. Educ.* 2021, 98 (3), 876–881.



#### 2020 

- 🧪🔬🏗️💻 | [ChemOS: An Orchestration Software to Democratize Autonomous Discovery](https://doi.org/10.1371/journal.pone.0229862). 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](https://doi.org/10.26434/chemrxiv.12097908.v1). Häse, F.; Tamayo-Mendoza, T.; Boixo, C.; Romero, J.; Roch, L.; Aspuru-Guzik, A. *ChemRxiv* 2020.



#### 2019 

- 🧪🔬🏗️💻 | [Rethinking a Timeless Titration Experimental Setup through Automation and Open-Source Robotic Technology: Making Titration Accessible for Students of All Abilities](https://doi.org/10.1021/acs.jchemed.9b00025). Soong, R.; Agmata, K.; Doyle, T.; Jenne, A.; Adamo, A.; Simpson, A. J. J. *Chem. Educ.* 2019, 96 (7), 1497–1501.



### Industry



Industry examples involving SDLs.



#### Cloud-based Labs

- [IBM RoboRXN](https://research.ibm.com/science/ibm-roborxn/)

- [Emerald Cloud Lab](https://www.emeraldcloudlab.com/)

- [Strateos](https://strateos.com/)

- [Culture Biosciences](https://www.culturebiosciences.com/)

- [Arctoris](https://dev.arctoris.com/)

- [Kebotix](https://www.kebotix.com)

- [CMU Cloud Lab](https://cloudlab.cmu.edu/)

- [Argonne National Laboratory](https://www.anl.gov/autonomous-discovery)



#### Software-as-a-Service (SaaS)

- [Atinary](https://atinary.com/)

- [IBM Accelerated Discovery](https://research.ibm.com/topics/accelerated-discovery)

- [Citrine Informatics](https://citrine.io/)

- [Sunthetics](https://sunthetics.io/)

- [Globus](https://www.globus.org/)



### Prospective

Ideas for SDLs.



- [Reproducible Sorbent Materials Foundry for Carbon Capture at Scale](https://doi.org/10.1016/j.xcrp.2022.101063). 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](https://doi.org/10.1016/j.matt.2022.08.017). 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](https://arxiv.org/abs/2302.04397). Kalinin, S. V., Ziatdinov, M., Ahmadi, M., Ghosh, A., Roccapriore, K., Liu, Y., & Vasudevan, R. K. (2023). *arXiv:2302.04397*.



## Software

Examples of [experimental orchestration](#experimental-orchestration-software), [optimization](#optimization), [information management](#information-management), and [other](#other) software.



### Workflow Orchestration



Experimental orchestration software for autonomously controlling laboratory experiments.



#### Experimental Science



- Alab Management [[code](https://github.com/CederGroupHub/alab_management)] [[docs](https://alab-management.readthedocs.io/en/latest/)]

- Bluesky [[code](https://github.com/bluesky/bluesky)] [[docs](https://blueskyproject.io/bluesky/)]

- HELAO [[code](https://github.com/helgestein/helao-pub)] [[paper](https://dx.doi.org/10.1002/admi.202101987)]

- ChemOS 2.0 [[code](https://github.com/malcolmsimgithub/ChemOS2.0)] [[paper](https://doi.org/10.1016/j.matt.2024.04.022)]

- Chemios [[code](https://github.com/Chemios/chemios)]

- ARES OS [[code](https://github.com/AFRL-ARES/ARES_OS)] [[paper](https://dx.doi.org/10.1557/s43577-021-00051-1)]

- PLACE [[code](https://github.com/PALab/place)] [[paper](https://doi.org/10.1177/2211068214553022)]

- XDL [[code](https://gitlab.com/croningroup/chemputer/xdl)] [[docs](https://croningroup.gitlab.io/chemputer/xdl/)] [[paper](https://dx.doi.org/10.1126/science.aav2211)]

- self-driving-lab-demo [[code](https://github.com/sparks-baird/self-driving-lab-demo)] [[docs](https://self-driving-lab-demo.readthedocs.io/)]

- NIMS-OS [[code](https://github.com/nimsos-dev/nimsos)] [[docs](https://nimsos-dev.github.io/nimsos/docs/en/index.html)] [[paper](https://doi.org/10.48550/arXiv.2304.13927)]



#### General Purpose



- Prefect [[code](https://github.com/PrefectHQ/prefect) [[docs](https://www.prefect.io/)]

- Node-RED [[code](https://github.com/node-red/node-red) [[docs](https://nodered.org/)]

- Robot Operating System (ROS) [[code](https://github.com/ros/ros)] [[docs](https://www.ros.org/)]

- Derived Information Framework [[code](https://github.com/cambridge-cares/TheWorldAvatar/tree/main/JPS_BASE_LIB/python_derivation_agent)] [[docs](https://github.com/cambridge-cares/TheWorldAvatar/blob/main/JPS_BASE_LIB/python_derivation_agent/README.md)] [[paper](https://doi.org/10.1016/j.future.2023.10.008)]



#### Communication Protocols/Server Frameworks

- MQTT [[code (Python interface)](https://github.com/eclipse/paho.mqtt.python)] [[docs](https://mqtt.org/)]

- SiLA2 (based on HTTP/2) [[code (Python interface)](https://gitlab.com/SiLA2/sila_python)] [[docs](https://sila-standard.com/standards/)]

- OPC-UA [[code (Python interface)](https://github.com/FreeOpcUa/python-opcua)] [[docs](https://opcfoundation.org/about/opc-technologies/opc-ua/)]

- Robot Operating System (ROS) [[code](https://github.com/ros/ros)] [[docs](https://www.ros.org/)]



See also [@sgbaird's lab-automation list](https://github.com/stars/sgbaird/lists/lab-automation) and [Awesome Workflow Repositories](https://meirwah.github.io/awesome-workflow-engines/).



### Optimization

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



#### Open-source

- [Adaptive Experimentation Platform (Ax)](https://ax.dev/) 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](https://botorch.org/) 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](https://github.com/dragonfly/dragonfly) is an open source python library for scalable Bayesian optimization with multi-objective and multi-fidelity support.

- [RayTune](https://docs.ray.io/en/latest/tune/index.html) offers experiment execution and hyperparameter tuning at any scale with many supported [search algorithms](https://docs.ray.io/en/latest/tune/api/suggestion.html) and [trial schedulers](https://docs.ray.io/en/latest/tune/api/schedulers.html) under a common interface.

- Aspuru-Guzik Group

  - [Atlas](https://github.com/rileyhickman/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](https://github.com/aspuru-guzik-group/chimera) is a hierarchy-based multi-objective optimization scalarizing function.

  - [Gryffin](https://github.com/aspuru-guzik-group/gryffin) enables Bayesian optimization of continuous and categorical variables with support for physicochemical descriptors and batch optimization.

  - [Gemini](https://github.com/aspuru-guzik-group/gemini) is a scalable multi-fidelity Bayesian optimization technique and is supported by Gryffin.

  - [Golem](https://github.com/aspuru-guzik-group/golem) is an algorithm that helps identify optimal solutions that are robust to input uncertainty (i.e., robust optimization).

  - [Phoenics](https://github.com/aspuru-guzik-group/phoenics) is a linear-scaling Bayesian optimization algorithm with support for batch and periodic parameter optimization.

  - [Anubis](https://github.com/aspuru-guzik-group/atlas-unknown-constraints) is a Bayesian optimization algorithm that models unknown feasibility constraints and incorporates it into the acquisition function

- [BoFire](https://github.com/experimental-design/bofire) is a **B**ayesian **O**ptimization **F**ramework **I**ntended for **R**eal **E**xperiments (under development) with support for advanced optimization tasks such as mixed variables, multiple objectives, and generic constraints.

- [NIMS-OS](https://github.com/nimsos-dev/nimsos) is a Python package (+GUI) for

  workflow orchestration and multi-objective optimization software that supports [BLOX](https://github.com/tsudalab/BLOX), [PDC](https://github.com/tsudalab/PDC),

  random exploration, and a multi-objective variant of [PHYSBO](https://github.com/issp-center-dev/PHYSBO).

- [SLAMD](https://github.com/BAMresearch/WEBSLAMD) - A web app leveraging data-driven design for cementitious materials via a digital lab twin, complemented by a (materials-agnostic) AI optimization feature. Features UI to interactively explore  design spaces. The web app uses Python and Javascript.

- [Summit](https://github.com/sustainable-processes/summit) is a set of tools for optimizing chemical processes with a wide variety of design of experiments (DoE) and adaptive design methods along with benchmarks.

- [GPax](https://github.com/ziatdinovmax/gpax) is a 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. It supports "deep kernel learning", structured probabilistic mean functions, hypothesis learning workflows, multitask, multifidelity, heteroscedastic, and vector BO and emphasizes user friendliness.

- [Bayesian Back End (BayBE)](https://github.com/emdgroup/baybe) is a open-source toolbox by [Merck KGaA](https://www.merckgroup.com/) for Bayesian optimization, featuring custom encodings, chemical knowledge integration, hybrid spaces, transfer learning, simulation tools, and robust, serializable code for real-world experimental campaigns.

- [Honegumi](https://honegumi.readthedocs.io/) ("ho-nay-goo-mee"), which means “skeletal framework” in Japanese, is a package for interactively creating minimal working examples for advanced Bayesian optimization topics.



#### Proprietary

- [ChemOS](https://chemos.io/)

- [The Citrine Platform](https://citrine.io/product/what-is-the-citrine-platform/)

- IBM Accelerated Discovery

  - [RXN for Chemistry](https://rxn.res.ibm.com/)

  - [Simulation Toolkit For Scientific Discovery (ST4SD)](https://st4sd.github.io/overview/)

  - [Generative Toolkit for Scientific Discovery](https://gt4sd.github.io/gt4sd-core/)

  - [Deep Search](https://ds4sd.github.io/) (semantic extraction from documents)



### Research Data Management



i.e., Electronic Lab Notebooks (ELNs) and Laboratory Information Management Systems (LIMS). You may also be interested in this [list of solutions by Labii](https://github.com/Labii/comparison-of-best-electronic-lab-notebook), of course keeping in mind that the list is compiled by a specific ELN company. See also this [ELN Finder tool](https://eln-finder.ulb.tu-darmstadt.de/home).



#### Open-source

- [eLabFTW](https://www.elabftw.net/)

- [NOMAD Oasis](https://nomad-lab.eu/nomad-lab/nomad-oasis.html) [[Example](https://www.youtube.com/watch?v=KVpUUJ5VFh4&t)]



#### Proprietary

- [SciNote](https://www.scinote.net/)

- [Uncountable](https://www.uncountable.com/)

- [Protocols.io](https://protocols.io)

- [Labii](https://www.labii.com/)

- [Scispot](https://www.scispot.com/)



### Other

- Benchmarking

  - [Olympus](https://github.com/aspuru-guzik-group/olympus) is a benchmarking framework based primarily on data collected from experimental self-driving lab setups.

- Functional Data Analysis

  - [Amplitude-Phase-Distance](https://github.com/kiranvad/Amplitude-Phase-Distance) is a Riemannian differential geometry toolbox to compute a `shape' distance between electromagnetic spectra, scattering, or diffraction profiles.

  - [autophasemap](https://github.com/pozzo-research-group/papers/tree/main/autophasemap) is a clustering and phase mapping toolbox using the amplitude phase distance for autonomous construction of phasemaps from spectra-like data.

- Instrument-specific drivers

  - Chemspyd [[code](https://gitlab.com/aspuru-guzik-group/self-driving-lab/instruments/chemspyd)] [[paper](https://chemrxiv.org/engage/chemrxiv/article-details/65d39af766c13817290035c1)] is a Python API for the Chemspeed AutoSuite software.



## Hardware



### Open-source



- [Science Jubilee](https://machineagency.github.io/science_jubilee/)



## People

WIP



https://acceleration.utoronto.ca/researcher



## Media

- [Self-driving Laboratories do Research on Autopilot](https://hackaday.com/2022/09/29/self-driving-laboratories-do-research-on-autopilot/). Hackaday 2022.

- [Lowe, D. The Downside of Chemistry Automation](https://www.science.org/content/blog-post/downside-chemistry-automation). (accessed 2022-08-26).



## Contribute



Contributions welcome! Read the [contribution guidelines](contributing.md) first.



## License

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