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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
- Role of AI in Experimental Materials Science
- Next-Generation Intelligent Laboratories for Materials Design and Manufacturing
- Toward Autonomous Laboratories: Convergence of Artificial Intelligence and Experimental Automation
- The Rise of Self-Driving Labs in Chemical and Materials Sciences
- The Digital Lab Framework as part of The World Avatar
- Research Acceleration in Self‐Driving Labs: Technological Roadmap toward Accelerated Materials and Molecular Discovery - Licona, F.; Abolhasani, M. *Advanced Intelligent Systems* 2022, 2200331.
- Artificial Intelligence for Materials Research at Extremes - 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
- Autonomous (AI-Driven) Materials Science
- Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab - 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
- Reaching Critical MASS: Crowdsourcing Designs for the next Generation of Materials Acceleration Platforms - Simpers, J.; Aspuru-Guzik, A.; Kalil, T.; Cranford, S. *Matter* 2022, 5 (7), 1972–1976.
- Defining Levels of Automated Chemical Design
- Toward Autonomous Materials Research: Recent Progress and Future Challenges - 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
- Enabling Modular Autonomous Feedback-Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration
- Flexible Automation Accelerates Materials Discovery
- Autonomous Experimentation Systems for Materials Development: A Community Perspective - 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
- Autonomous Discovery in the Chemical Sciences Part II: Outlook
- Autonomous Discovery in the Chemical Sciences Part I: Progress
- Materials Acceleration Platforms: On the Way to Autonomous Experimentation - 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
- The Internet of Things Comes to the Lab
- represent full autonomy vs. manual intervention
- A dynamic knowledge graph approach to distributed self-driving laboratories
- Powder-Bot: A Modular Autonomous Multi-Robot Workflow for Powder X-Ray Diffraction
- A Robotic Platform for the Synthesis of Colloidal Nanocrystals - F. *Nat. Synth* 2023.
- Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back - 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
- NIMS-OS: An automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science
- A Self-Driving Laboratory Designed to Accelerate the Discovery of Adhesive Materials
- A self-driving laboratory advances the Pareto front for material properties
- Autonomous retrosynthesis of gold nanoparticles via spectral shape matching
- Physics Discovery in Nanoplasmonic Systems via Autonomous Experiments in Scanning Transmission Electron Microscopy
- Autonomous Materials Synthesis via Hierarchical Active Learning of Nonequilibrium Phase Diagrams - 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 - W.; Shutt, K.; Lin, J. *ACS Appl. Mater. Interfaces* 2021, 13 (45), 53485–53491.
- Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy
- Realization of closed-loop optimization of epitaxial titanium nitride thin-film growth via machine learning
- Toward Autonomous Additive Manufacturing: Bayesian Optimization on a 3D Printer
- Using simulation to accelerate autonomous experimentation: A case study using mechanics
- Autonomous Discovery of Battery Electrolytes with Robotic Experimentation and Machine Learning
- Self-Driving Laboratory for Accelerated Discovery of Thin-Film Materials - Guzik, A.; Hein, J. E.; Berlinguette, C. P. *Sci. Adv.* 2020, 6 (20), eaaz8867.
- ChemOS: An Orchestration Software to Democratize Autonomous Discovery - 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
- Autonomous materials synthesis by machine learning and robotics
- A Bayesian experimental autonomous researcher for mechanical design
- Networking Chemical Robots for Reaction Multitasking
- Autonomy in Materials Research: A Case Study in Carbon Nanotube Growth
- Evolution of Oil Droplets in a Chemorobotic Platform
- Automated PH Adjustment Driven by Robotic Workflows and Active Machine Learning - Bischof, P.; Lapkin, A. A. *Chemical Engineering Journal* 2023, 451, 139099.
- Build Instructions for Closed-Loop Spectroscopy Lab: Light-Mixing Demo
- Driving school for self-driving labs
- What Is a Minimal Working Example for a Self-Driving Laboratory?
- The LEGOLAS Kit: A Low-Cost Robot Science Kit for Education with Symbolic Regression for Hypothesis Discovery and Validation
- Augmented Titration Setup for Future Teaching Laboratories
- ChemOS: An Orchestration Software to Democratize Autonomous Discovery - 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 - 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
- IBM RoboRXN
- Emerald Cloud Lab
- Strateos
- Culture Biosciences
- Arctoris
- Kebotix
- CMU Cloud Lab
- Argonne National Laboratory
- Atinary
- IBM Accelerated Discovery
- Citrine Informatics
- Sunthetics
- Globus
- Reproducible Sorbent Materials Foundry for Carbon Capture at Scale - 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 - 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
- Delivering Real-Time Multi-Modal Materials Analysis with Enterprise Beamlines - PHYS-SC 2022, 3 (11).
- An Automated Biomateriomics Platform for Sustainable Programmable Materials Discovery - --->
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- @sgbaird's lab-automation list - workflow-engines/).
- Adaptive Experimentation Platform (Ax) - 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 - averse Bayesian optimization and constraint active search.
- Dragonfly - objective and multi-fidelity support.
- RayTune
- Atlas - world experimental science problems: mixed parameters, multi-objective, noisy, constrained, multi-fidelity, and meta-learning optimization along with search space expansion/contraction. [WIP]
- Chimera - based multi-objective optimization scalarizing function.
- Gryffin
- Gemini - fidelity Bayesian optimization technique and is supported by Gryffin.
- Golem
- Phoenics - scaling Bayesian optimization algorithm with support for batch and periodic parameter optimization.
- Anubis
- BoFire
- NIMS-OS
- BLOX
- PHYSBO
- SLAMD - 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
- GPax - 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) - 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 - nay-goo-mee"), which means “skeletal framework” in Japanese, is a package for interactively creating minimal working examples for advanced Bayesian optimization topics.
- ChemOS
- The Citrine Platform
- RXN for Chemistry
- Simulation Toolkit For Scientific Discovery (ST4SD)
- Generative Toolkit for Scientific Discovery
- Deep Search
- list of solutions by Labii - finder.ulb.tu-darmstadt.de/home).
- eLabFTW
- NOMAD Oasis
- SciNote
- Uncountable
- Protocols.io
- Labii
- Scispot
- Olympus - driving lab setups.
- Amplitude-Phase-Distance
- autophasemap - like data.
- [code - management.readthedocs.io/en/latest/)]
- Science Jubilee
- Self-driving Laboratories do Research on Autopilot
- Lowe, D. The Downside of Chemistry Automation - 08-26).
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Programming Languages
Keywords
machine-learning
8
bayesian-optimization
7
python
7
optimization
4
experimental-design
4
automation
3
active-learning
3
orchestration
2
data-science
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materials-science
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deep-learning
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chemistry
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workflow
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optimization-algorithms
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self-driving-lab
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rpi-pico
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raspberry-pi
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experiment-planning
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phoenics
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self-driving-laboratories
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cheminformatics
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multiobjective-optimization
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drug-discovery
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nelder-mead
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neural-networks
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self-optimization
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snobfit
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bluesky
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dataacquisition
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chemyx
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harvard-apparatus
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high-throughput-screening
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iot
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laboratory
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modbus
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omega
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pump-control
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science
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syringe-pump
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adaptive-design
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as7341
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circuitpython
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closed-loop
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internet-of-laboratory-things
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materials-informatics
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micropython
1
neopixel
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optics
1
pico-w
1