AlabOS: a Python-based reconfigurable workflow management framework for autonomous laboratories

Fei, Yuxing; Rendy, Bernardus; Kumar, Rishi; Dartsi, Olympia; Sahasrabuddhe, Hrushikesh P.; McDermott, Matthew J.; Wang, Zheren; Szymanski, Nathan J.; Walters, Lauren N.; Milsted, David; Zeng, Yan; Jain, Anubhav; Ceder, Gerbrand

doi:10.1039/D4DD00129J

Title: AlabOS: a Python-based reconfigurable workflow management framework for autonomous laboratories

Journal Article · 06 November 2024 · Digital Discovery

DOI: https://doi.org/10.1039/D4DD00129J · OSTI ID:2476439

^[1];

^[1]; Kumar, Rishi ^[2]; Dartsi, Olympia ^[2]; Sahasrabuddhe, Hrushikesh P. ^[3];

^[4];

^[1]; Szymanski, Nathan J. ^[1];

^[4];

^[4]; Zeng, Yan ^[5]; Jain, Anubhav ^[2];

^[1]

Department of Materials Science & Engineering, University of California, Berkeley, CA 94720, USA, Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Department of Materials Science & Engineering, University of California, Berkeley, CA 94720, USA, Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA, Department of Chemistry & Biochemistry, Florida State University, Tallahassee, Florida 32306, USA

AlabOS is a workflow orchestration framework designed to address the increased complexity in autonomous laboratories, featuring a reconfigurable experiment workflow model and a resource reservation mechanism.

View Journal Article

Cite

Export

Save

Sponsoring Organization:: USDOE

Grant/Contract Number:: NONE; AC02-05CH11231

OSTI ID:: 2476439

Journal Information:: Digital Discovery, Journal Name: Digital Discovery Journal Issue: 11 Vol. 3; ISSN DDIIAI; ISSN 2635-098X

Publisher:: Royal Society of Chemistry (RSC)Copyright Statement

Country of Publication:: United Kingdom

Language:: English

References (48)

Enabling Modular Autonomous Feedback‐Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration Rahmanian, Fuzhan; Flowers, Jackson; Guevarra, Dan Advanced Materials Interfaces, Vol. 9, Issue 8 https://doi.org/10.1002/admi.202101987	journal	January 2022
Data‐Driven Materials Science: Status, Challenges, and Perspectives Himanen, Lauri; Geurts, Amber; Foster, Adam Stuart Advanced Science, Vol. 6, Issue 21 https://doi.org/10.1002/advs.201900808	journal	September 2019
FireWorks: a dynamic workflow system designed for high-throughput applications: FireWorks: A Dynamic Workflow System Designed for High-Throughput Applications Jain, Anubhav; Ong, Shyue Ping; Chen, Wei Concurrency and Computation: Practice and Experience, Vol. 27, Issue 17 https://doi.org/10.1002/cpe.3505	journal	May 2015
Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD) Saal, James E.; Kirklin, Scott; Aykol, Muratahan JOM, Vol. 65, Issue 11 https://doi.org/10.1007/s11837-013-0755-4	journal	September 2013
Universal self-driving laboratory for accelerated discovery of materials and molecules Epps, Robert W.; Volk, Amanda A.; Ibrahim, Malek Y. S. Chem, Vol. 7, Issue 10 https://doi.org/10.1016/j.chempr.2021.09.004	journal	October 2021
AFLOW: An automatic framework for high-throughput materials discovery Curtarolo, Stefano; Setyawan, Wahyu; Hart, Gus L. W. Computational Materials Science, Vol. 58 https://doi.org/10.1016/j.commatsci.2012.02.005	journal	June 2012
Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows Mathew, Kiran; Montoya, Joseph H.; Faghaninia, Alireza Computational Materials Science, Vol. 139 https://doi.org/10.1016/j.commatsci.2017.07.030	journal	November 2017
Perspectives for self-driving labs in synthetic biology Martin, Hector G.; Radivojevic, Tijana; Zucker, Jeremy Current Opinion in Biotechnology, Vol. 79 https://doi.org/10.1016/j.copbio.2022.102881	journal	February 2023
Application of high-throughput first-principles calculations in ceramic innovation Liu, Bin; Zhao, Juanli; Liu, Yuchen Journal of Materials Science & Technology, Vol. 88 https://doi.org/10.1016/j.jmst.2021.01.071	journal	October 2021
Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing Correa-Baena, Juan-Pablo; Hippalgaonkar, Kedar; van Duren, Jeroen Joule, Vol. 2, Issue 8 https://doi.org/10.1016/j.joule.2018.05.009	journal	August 2018
ChemOS 2.0: An orchestration architecture for chemical self-driving laboratories Sim, Malcolm; Vakili, Mohammad Ghazi; Strieth-Kalthoff, Felix Matter https://doi.org/10.1016/j.matt.2024.04.022	journal	May 2024
Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation Xie, Yunchao; Sattari, Kianoosh; Zhang, Chi Progress in Materials Science, Vol. 132 https://doi.org/10.1016/j.pmatsci.2022.101043	journal	February 2023
Progress toward Solid State Synthesis by Design Chamorro, Juan R.; McQueen, Tyrel M. Accounts of Chemical Research, Vol. 51, Issue 11 https://doi.org/10.1021/acs.accounts.8b00382	journal	October 2018
Probabilistic Deep Learning Approach to Automate the Interpretation of Multi-phase Diffraction Spectra Szymanski, Nathan J.; Bartel, Christopher J.; Zeng, Yan Chemistry of Materials, Vol. 33, Issue 11 https://doi.org/10.1021/acs.chemmater.1c01071	journal	May 2021
Rational Solid-State Synthesis Routes for Inorganic Materials Aykol, Muratahan; Montoya, Joseph H.; Hummelshøj, Jens Journal of the American Chemical Society, Vol. 143, Issue 24 https://doi.org/10.1021/jacs.1c04888	journal	June 2021
Machine-learning-assisted materials discovery using failed experiments Raccuglia, Paul; Elbert, Katherine C.; Adler, Philip D. F. Nature, Vol. 533, Issue 7601 https://doi.org/10.1038/nature17439	journal	May 2016
The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies Kirklin, Scott; Saal, James E.; Meredig, Bryce npj Computational Materials, Vol. 1, Issue 1 https://doi.org/10.1038/npjcompumats.2015.10	journal	December 2015
A bridge for accelerating materials by design Sumpter, Bobby G.; Vasudevan, Rama K.; Potok, Thomas npj Computational Materials, Vol. 1, Issue 1 https://doi.org/10.1038/npjcompumats.2015.8	journal	November 2015
On-the-fly closed-loop materials discovery via Bayesian active learning Kusne, A. Gilad; Yu, Heshan; Wu, Changming Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-19597-w	journal	November 2020
Autonomous and dynamic precursor selection for solid-state materials synthesis Szymanski, Nathan J.; Nevatia, Pragnay; Bartel, Christopher J. Nature Communications, Vol. 14, Issue 1 https://doi.org/10.1038/s41467-023-42329-9	journal	October 2023
Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering Stanev, Valentin; Vesselinov, Velimir V.; Kusne, A. Gilad npj Computational Materials, Vol. 4, Issue 1 https://doi.org/10.1038/s41524-018-0099-2	journal	August 2018
Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks Oviedo, Felipe; Ren, Zekun; Sun, Shijing npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0196-x	journal	May 2019
Recent advances and applications of machine learning in solid-state materials science Schmidt, Jonathan; Marques, Mário R. G.; Botti, Silvana npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0221-0	journal	August 2019
Common workflows for computing material properties using different quantum engines Huber, Sebastiaan P.; Bosoni, Emanuele; Bercx, Marnik npj Computational Materials, Vol. 7, Issue 1 https://doi.org/10.1038/s41524-021-00594-6	journal	August 2021
Recent advances and applications of deep learning methods in materials science Choudhary, Kamal; DeCost, Brian; Chen, Chi npj Computational Materials, Vol. 8, Issue 1 https://doi.org/10.1038/s41524-022-00734-6	journal	April 2022
An autonomous portable platform for universal chemical synthesis Manzano, J. Sebastián; Hou, Wenduan; Zalesskiy, Sergey S. Nature Chemistry, Vol. 14, Issue 11 https://doi.org/10.1038/s41557-022-01016-w	journal	October 2022
Controlling an organic synthesis robot with machine learning to search for new reactivity Granda, Jarosław M.; Donina, Liva; Dragone, Vincenza Nature, Vol. 559, Issue 7714 https://doi.org/10.1038/s41586-018-0307-8	journal	July 2018
Machine learning for molecular and materials science Butler, Keith T.; Davies, Daniel W.; Cartwright, Hugh Nature, Vol. 559, Issue 7715 https://doi.org/10.1038/s41586-018-0337-2	journal	July 2018
A mobile robotic chemist Burger, Benjamin; Maffettone, Phillip M.; Gusev, Vladimir V. Nature, Vol. 583, Issue 7815 https://doi.org/10.1038/s41586-020-2442-2	journal	July 2020
An autonomous laboratory for the accelerated synthesis of novel materials Szymanski, Nathan J.; Rendy, Bernardus; Fei, Yuxing Nature, Vol. 624, Issue 7990 https://doi.org/10.1038/s41586-023-06734-w	journal	November 2023
Materials Cloud, a platform for open computational science Talirz, Leopold; Kumbhar, Snehal; Passaro, Elsa Scientific Data, Vol. 7, Issue 1 https://doi.org/10.1038/s41597-020-00637-5	journal	September 2020
Automating crystal-structure phase mapping by combining deep learning with constraint reasoning Chen, Di; Bai, Yiwei; Ament, Sebastian Nature Machine Intelligence, Vol. 3, Issue 9 https://doi.org/10.1038/s42256-021-00384-1	journal	September 2021
The rise of self-driving labs in chemical and materials sciences Abolhasani, Milad; Kumacheva, Eugenia Nature Synthesis, Vol. 2, Issue 6 https://doi.org/10.1038/s44160-022-00231-0	journal	January 2023
Optimal thermodynamic conditions to minimize kinetic by-products in aqueous materials synthesis Wang, Zheren; Sun, Yingzhi; Cruse, Kevin Nature Synthesis, Vol. 3, Issue 4 https://doi.org/10.1038/s44160-023-00479-0	journal	January 2024
Deep convolutional neural networks for Raman spectrum recognition: a unified solution Liu, Jinchao; Osadchy, Margarita; Ashton, Lorna The Analyst, Vol. 142, Issue 21 https://doi.org/10.1039/C7AN01371J	journal	January 2017
Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories Häse, Florian; Roch, Loïc M.; Aspuru-Guzik, Alán Chemical Science, Vol. 9, Issue 39 https://doi.org/10.1039/C8SC02239A	journal	January 2018
Towards a modular architecture for science factories Vescovi, Rafael; Ginsburg, Tobias; Hippe, Kyle Digital Discovery, Vol. 2, Issue 6 https://doi.org/10.1039/D3DD00142C	journal	January 2023
Orchestrating nimble experiments across interconnected labs Guevarra, Dan; Kan, Kevin; Lai, Yungchieh Digital Discovery, Vol. 2, Issue 6 https://doi.org/10.1039/D3DD00166K	journal	January 2023
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy APL Materials, Vol. 1, Issue 1 https://doi.org/10.1063/1.4812323	journal	July 2013
Bluesky's Ahead: A Multi-Facility Collaboration for an a la Carte Software Project for Data Acquisition and Management Allan, Daniel; Caswell, Thomas; Campbell, Stuart Synchrotron Radiation News, Vol. 32, Issue 3 https://doi.org/10.1080/08940886.2019.1608121	journal	May 2019
ARChemist: Autonomous Robotic Chemistry System Architecture Fakhruldeen, Hatem; Pizzuto, Gabriella; Glowacki, Jakub 2022 International Conference on Robotics and Automation (ICRA) https://doi.org/10.1109/ICRA46639.2022.9811996	conference	May 2022
Organic synthesis in a modular robotic system driven by a chemical programming language Steiner, Sebastian; Wolf, Jakob; Glatzel, Stefan Science, Vol. 363, Issue 6423 https://doi.org/10.1126/science.aav2211	journal	November 2018
Synthesis of proteins by automated flow chemistry Hartrampf, N.; Saebi, A.; Poskus, M. Science, Vol. 368, Issue 6494 https://doi.org/10.1126/science.abb2491	journal	May 2020
Delocalized, asynchronous, closed-loop discovery of organic laser emitters Strieth-Kalthoff, Felix; Hao, Han; Rathore, Vandana Science, Vol. 384, Issue 6697 https://doi.org/10.1126/science.adk9227	journal	May 2024
Foundations of JSON Schema Pezoa, Felipe; Reutter, Juan L.; Suarez, Fernando WWW '16: 25th International World Wide Web Conference, Proceedings of the 25th International Conference on World Wide Web https://doi.org/10.1145/2872427.2883029	conference	April 2016
An empirical characterization of IFTTT Mi, Xianghang; Qian, Feng; Zhang, Ying Proceedings of the 2017 Internet Measurement Conference https://doi.org/10.1145/3131365.3131369	conference	November 2017
Opportunities and Challenges for Machine Learning in Materials Science Morgan, Dane; Jacobs, Ryan Annual Review of Materials Research, Vol. 50, Issue 1 https://doi.org/10.1146/annurev-matsci-070218-010015	journal	July 2020
Experiment Specification, Capture and Laboratory Automation Technology (ESCALATE): a software pipeline for automated chemical experimentation and data management Pendleton, Ian M.; Cattabriga, Gary; Li, Zhi MRS Communications, Vol. 9, Issue 3 https://doi.org/10.1557/mrc.2019.72	journal	June 2019

Similar Records

Autonomic Management of Application Workflows on Hybrid Computing Infrastructure

Journal Article · 2010 · Scientific Programming · OSTI ID:1243135

Kim, Hyunjoo; el-Khamra, Yaakoub; Rodero, Ivan; +2 more

DYFLOW: A flexible framework for orchestrating scientific workflows on supercomputers

Conference · 2021 · OSTI ID:1827006

Singhal, Swati; Sussman, Alan; Wolf, Matthew; +2 more

Towards a Standard Process Management Infrastructure for Workflows Using Python

Conference · 2023 · OSTI ID:1969826

Elwasif, Wael; Naughton III, Thomas; Baker, Matthew

Title: AlabOS: a Python-based reconfigurable workflow management framework for autonomous laboratories

Citation Formats

References (48)

Similar Records

Related Subjects