What is missing in autonomous discovery: open challenges for the community

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Abstract

Self-driving labs (SDLs) leverage combinations of artificial intelligence, automation, and advanced computing to accelerate scientific discovery.

Abstract

Self-driving labs (SDLs) leverage combinations of artificial intelligence, automation, and advanced computing to accelerate scientific discovery. The promise of this field has given rise to a rich community of passionate scientists, engineers, and social scientists, as evidenced by the development of the Acceleration Consortium and recent Accelerate Conference. Despite its strengths, this rapidly developing field presents numerous opportunities for growth, challenges to overcome, and potential risks of which to remain aware. This community perspective builds on a discourse instantiated during the first Accelerate Conference, and looks to the future of self-driving labs with a tempered optimism. Incorporating input from academia, government, and industry, we briefly describe the current status of self-driving labs, then turn our attention to barriers, opportunities, and a vision for what is possible. Our field is delivering solutions in technology and infrastructure, artificial intelligence and knowledge generation, and education and workforce development. In the spirit of community, we intend for this work to foster discussion and drive best practices as our field grows.

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Most cited references 171

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The Protein Data Bank.

H M Berman, J Westbrook, Z. Feng … (2000)

The Protein Data Bank (PDB; http://www.rcsb.org/pdb/ ) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.

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Array programming with NumPy

Charles R. Harris, K Jarrod Millman, Stéfan van der Walt … (2020)

Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves 1 and in the first imaging of a black hole 2 . Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.

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The FAIR Guiding Principles for scientific data management and stewardship

Mark D Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg … (2016)

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

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Author and article information

Contributors

Phillip M. Maffettone: (View ORCID Profile)

Pascal Friederich: (View ORCID Profile)

Ben Blaiszik: (View ORCID Profile)

Keith A. Brown: (View ORCID Profile)

Stuart I. Campbell: (View ORCID Profile)

Rebecca L. Davis: (View ORCID Profile)

Navid Haghmoradi: (View ORCID Profile)

Mark Hereld: (View ORCID Profile)

Ha-Kyung Kwon: (View ORCID Profile)

Gabriella Pizzuto: (View ORCID Profile)

Casper Steinmann: (View ORCID Profile)

Journal

Journal ID (publisher-id): DDIIAI

Title: Digital Discovery

Abbreviated Title: Digital Discovery

Publisher: Royal Society of Chemistry (RSC)

ISSN (Electronic): 2635-098X

Publication date Created: December 04 2023

Publication date (Electronic): 2023

Volume: 2

Issue: 6

Pages: 1644-1659

Affiliations

[1 ]National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, NY 11973, USA

[2 ]BigHat Biosciences, San Mateo, CA 94403, USA

[3 ]Karlsruhe Institute of Technology, Institute of Nanotechnology, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany

[4 ]Karlsruhe Institute of Technology, Institute of Theoretical Informatics, Engler-Bunte-Ring 8, 76131, Karlsruhe, Germany

[5 ]University of Utah, Salt Lake City, UT 84108, USA

[6 ]University of Chicago, Chicago, IL 60637, USA

[7 ]Argonne National Laboratory, Lemont, IL 60439, USA

[8 ]Boston University, Boston, MA 02215, USA

[9 ]Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

[10 ]University of Manitoba, Winnipeg, Manitoba R3T 2N2, Canada

[11 ]Materials Measurement Science Divison, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA

[12 ]Karlsruhe Institute of Technology, Institute of Biological and Chemical Systems, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany

[13 ]Toyota Research Institute, Los Altos, CA 94022, USA

[14 ]University of Liverpool, Liverpool, L69 3BX, UK

[15 ]Stanford University, Stanford, CA, 94305, USA

[16 ]Department of Chemistry and Bioscience, Aalborg University, 9220, Aalborg, Denmark

[17 ]University of Washington, Seattle, WA, 98195, USA

Article

DOI: 10.1039/D3DD00143A

SO-VID: 9bdcf4eb-4b7e-4872-8895-16774aa85667

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http://creativecommons.org/licenses/by/3.0/

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What is missing in autonomous discovery: open challenges for the community

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Most cited references 171

The Protein Data Bank.

Array programming with NumPy

The FAIR Guiding Principles for scientific data management and stewardship

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