A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery

Benjamin Mintz; Dionysios A. Antonopoulos; Frederic Suter; Ian T. Foster; Laura Biven; Leslie Hamilton; Michela Taufer; Milad Abolhasani; Mitra Taheri; Newell R. Washburn

REVIEW 3 major objections 5 minor 1 cited by

A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Autonomous labs must network to speed discovery from decades to months.

desk verdict A well-organized roadmap for interconnecting autonomous labs that is honest about its open problems but overclaims acceleration; worth reading as a community statement, not as evidence. read the letter →

arxiv 2506.17510 v1 pith:N4SC24TM submitted 2025-06-20 cs.CY cs.DCphysics.soc-ph

Rafael Ferreira da Silva , Milad Abolhasani , Dionysios A. Antonopoulos , Laura Biven , Ryan Coffee , Ian T. Foster , Leslie Hamilton , Shantenu Jha

show 10 more authors

Theresa Mayer Benjamin Mintz Robert G. Moore Salahudin Nimer Noah Paulson Woong Shin Frederic Suter Mitra Taheri Michela Taufer Newell R. Washburn

This is my paper · ORCID

classification cs.CYcs.DCphysics.soc-ph

keywords AutonomousScienceDiscoveryScientificWorkflowsLabsoftheFutureself-drivinglaboratoriesmulti-agentsystemsFAIRdatainteroperability

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that autonomous laboratories, however powerful in isolation, cannot deliver on their promise until they can work together across institutional boundaries. It proposes AISLE, a grassroots network that would connect instruments, data, agents, and people through five coordinated dimensions: instrument and cyberinfrastructure integration, agent-driven data management, AI-agent orchestration, interoperable communication standards, and education. If the roadmap is realized, the authors claim discovery cycles shrink from decades to months, and research spaces closed to traditional approaches become accessible to a wider set of institutions. A sympathetic reader would take this as a design argument: the bottleneck is not any single lab's automation but the missing fabric that lets labs share experiments, data, and decisions.

What carries the argument

The load-bearing object is the AISLE network architecture: a distributed data fabric in which autonomous agents control instruments, curate data, and coordinate experiments across institutions through standardized communication protocols. Five layers work together—instrument and cyberinfrastructure integration, agent-driven data management, AI-agent orchestration, interoperable agent communication, and education and workforce development—each with explicit milestones (M1–M14). The mechanism that carries the argument is the assumption that these layers, once connected, let an experiment begun in one lab be continued, characterized, and simulated in others without human handoff.

What would settle it

A multi-institution pilot in which two or more labs attempt an end-to-end autonomous synthesis-and-characterization workflow would settle the claim: if the experiment cannot complete without manual intervention, or if LLM-orchestrated decisions fall below the paper's own target of greater than 95% experimental correctness with verification tools (milestone M8), the roadmap's promise of decades-to-months discovery is not met.

Watch

Extended reading notes

Core claim

The paper's central claim is that the next leap in scientific discovery comes from interconnecting autonomous laboratories into a single ecosystem rather than from improving any single automated lab. The AISLE vision defines five critical dimensions—cross-institutional equipment orchestration, FAIR-compliant agent-driven data management, AI-agent orchestration grounded in scientific principles, interoperable agent communication interfaces, and AI/ML-integrated education—and asserts that together they will transform fragmented capabilities into a unified system. The stated payoff is concrete: research that now takes decades would take months, and capabilities now confined to a few well-equipped institutions would become broadly accessible. The paper also commits to a series of milestones, from an instrument API consortium to zero-trust communication infrastructure, as the measurable path to that goal.

Load-bearing premise

The roadmap stands on the premise that institutions with proprietary instruments, conflicting security policies, and legal constraints can agree on interoperable standards, and that probabilistic AI agents can be made reliable enough to run physical equipment in real time.

Editorial extensions

If this is right

Cross-institutional autonomous workflows become routine, so an experiment can start in one laboratory and be characterized, simulated, or extended in another without human handoff.
Standardized instrument APIs and a distributed data fabric make advanced instrumentation accessible to resource-constrained institutions, not just major research facilities.
LLM-based agents, constrained by verification tools and digital twins, can orchestrate experiments with reproducible, physics-grounded decisions rather than uncontrolled probabilistic outputs.
Education shifts toward human-AI collaboration competencies, changing how scientists are trained and assessed in autonomous laboratory environments.
The paper's milestones, such as a 3x speedup over manual orchestration and greater than 95% experimental correctness with verification tools, become testable benchmarks for federated autonomous science.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The paper leaves implicit that the hardest constraint may be governance, not technology: without resolved intellectual-property and liability rules, even flawless interoperability standards will not move experiments across institutional boundaries.
A natural near-term test of the vision is whether an instrument API consortium can onboard even a handful of commercial vendors; that single adoption signal would predict whether the wider fabric is feasible.
If the agent fabric matures, it could extend beyond materials laboratories to link simulation facilities, observatories, and clinical sites, turning the architecture into a general infrastructure for autonomous science rather than a domain-specific network.
The milestone target of greater than 95% experimental correctness for LLM-orchestrated workflows offers a quantitative way to judge whether AI agents have crossed the reliability threshold the roadmap depends on.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a position paper/roadmap, not a scientific result, and that's okay. What's genuinely useful is the synthesis: it names five real dimensions—instrument integration, data management, agent orchestration, communication standards, education—and gives a decent state-of-the-art per dimension with citations to INTERSECT, MADSci, ChemOS, Academy, and others. The authors also deserve credit for explicitly listing challenges that most vision papers gloss over: IP and liability constraints in §3.1, and the probabilistic, hard-to-verify nature of LLM agents in §3.3. The milestone list (M1–M14) is concrete enough to be falsifiable, which is more than most roadmaps offer.

Soft spots are real but not fatal to the paper's stated purpose. The headline claim that AISLE 'accelerates discovery from decades to months' is unsupported; there is no mechanism or measurement behind it, just a vision statement. The authors seem aware, since the challenges sections acknowledge that LLM agents are difficult to verify and that there are no guarantees of physics grounding, but the abstract and conclusion still make the strong claim. The stress-test note is right: M8 targets '>95% experimental correctness' without describing a verification method—digital twins, formal methods, and symbolic verification are gestured at, not specified. Similarly, IP and liability are called critical barriers but no governance model is proposed. These are gaps in a roadmap, not internal contradictions; I don't think the paper's central argument collapses, because the central argument is 'this is worth building,' and the challenges sections support that by showing the work is hard and open.

One more minor point: the paper is written as though the ecosystem will happen ('AISLE will transform...'), which reads as promotional in places. But the references are legitimate, and the self-citations point to independently published work, so no problem there.

Who is this for? People planning autonomous lab infrastructure, program managers, and researchers who want a compact survey of the field's open problems. It deserves a serious referee—a roadmap paper can be peer-reviewed—but it should be framed as a community roadmap, not as evidence that the acceleration claim is achievable. If I handled it, I'd send it out with a request to soften the decades-to-months framing and, if kept, to tie it explicitly to the unmet milestones.

Referee Report

3 major / 5 minor

Summary. The paper proposes AISLE (Autonomous Interconnected Science Lab Ecosystem), a grassroots network intended to connect autonomous laboratories across institutions so that heterogeneous instruments, data, and AI agents can work together to accelerate scientific discovery. It identifies five critical dimensions: instrument and cyberinfrastructure integration, agent-driven data management, AI-agent-driven autonomous orchestration, interoperable agent communication standards, and education/workforce development. For each dimension, the paper gives a brief state of the art, lists challenges, proposes research priorities, and defines quantitative milestones M1 through M14. The stated central promise is that this ecosystem will shorten discovery cycles from decades to months and enable previously inaccessible research spaces.

Significance. If the AISLE vision is realized, the paper addresses a real and timely bottleneck: autonomous laboratories currently operate as isolated islands, and cross-institutional orchestration of instruments, data, and AI agents would be genuinely valuable for materials discovery, chemistry, and other data-intensive sciences. The paper's main strengths are its synthesis of a broad body of existing work (INTERSECT, DOE autonomous discovery initiatives, MADSci, ChemOS, NSDF, FAIR, and many others), its explicit enumeration of non-technical barriers such as intellectual property and liability, and its honest acknowledgment in Section 3.3 that LLM-based agents are probabilistic, difficult to verify, and not guaranteed to be grounded in physics. The milestone structure gives the community a concrete starting point for discussion. However, the paper contains no measurements, derivations, or completed pilot studies; its headline acceleration claim is aspirational rather than established. The value of the paper is therefore as a community roadmap and agenda-setting document rather than as a demonstration of the proposed acceleration.

major comments (3)

[Abstract; Section 2; Section 4] The central claim that AISLE 'accelerates discovery from decades to months' is asserted without supporting evidence, references to completed work, or a mechanistic argument. The paper's own Section 3.3 states that LLM-based agents 'are probabilistic in nature, higher-latency, and resource intensive compared to traditional methods, and are difficult to verify,' and that 'there are no guarantees whether the solutions driven by these systems would be grounded in scientific knowledge and physics.' Because the headline claim depends on exactly this unresolved capability, the abstract and conclusion overstate what the roadmap can establish. The authors should either soften these assertions to clearly framed hypotheses or provide a concrete argument, with references to demonstrated subsystems, for how the decade-to-months reduction would be achieved.
[Section 3.1; Section 3.2; Milestones M2, M6, M10, M11] The paper identifies critical organizational and governance barriers but does not propose solutions. Section 3.1 states that 'intellectual property management and liability concerns when cross-institutional failures occur... will significantly constrain real-world deployments,' and Section 3.2 lists privacy and regulatory constraints such as HIPAA as barriers to federated data sharing. Yet the milestones assume cross-institutional orchestration (M2), federated data sharing (M6), and federated identity integration (M10, M11) will be achievable without addressing these governance, legal, and policy questions. Since the feasibility of the entire network depends on these non-technical issues, the roadmap should either propose concrete governance mechanisms or explicitly mark them as open research and policy problems with a timeline for resolution.
[Section 3.3; Milestones M8 and M9] The quantitative milestones are not falsifiable as written. Milestone M8 targets a '3x speedup over manual orchestration and >95% experimental correctness versus agent usage without verification tools,' but 'experimental correctness' is never defined, no baseline or measurement protocol is specified, and no methodology is given for how the comparison would be conducted. Milestone M9 targets '>30% fewer experiments' with '>90% scientist approval of reasoning traces,' again without defining the metrics or evaluation procedure. Because these milestones are central to the claim of accelerated discovery, the paper should provide at least a preliminary definition of the metrics, the experimental design, and the validation approach, or reframe the milestones as qualitative goals.

minor comments (5)

[Abstract] The phrase 'a unified system that shorten the path' has a subject-verb agreement error; it should read 'that shortens the path.'
[Section 3.3] The text 'Smart Dope, which navigates10 13 possible synthesis conditions' is missing the superscript formatting and should read 'navigates 10^13 possible synthesis conditions.'
[Figure 1] The figure caption lists five dimensions, but the diagram's repeated 'agent' labels and the central 'DISTRIBUTED DATA FABRIC' box make it hard to see how the five dimensions relate to the data fabric; a clearer layout or an annotated callout for each dimension would improve readability.
[Section 3.1 and Section 3.4] The Academy middleware is described in both Section 3.1 and Section 3.4, and the reference numbering appears inconsistent: Section 3.1 cites it as [19], while Section 3.4 cites it as [18]. The authors should harmonize the citations.
[Throughout] The paper uses 'Brief State-of-the-art' as a heading in some sections but not others; using a consistent heading style, such as 'State of the Art' for every dimension, would improve uniformity.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a community roadmap whose central claims are aspirational, not derived from fitted inputs or self-cited uniqueness results.

full rationale

The paper does not present a derivation chain, fitted parameters, or predictive equations. Its central claim—that AISLE 'accelerates discovery from decades to months'—is an aspirational vision statement, not an output obtained from inputs by construction. The milestones (e.g., M8's '3x speedup' and '>95% experimental correctness') are proposed targets for future work, not predictions generated from a model or from fitted data. Self-citations appear (e.g., [7] Ferreira da Silva et al. 2024 workshop report, [16] Mintz 2023 on INTERSECT), but they are used only as contextual state-of-the-art references and are not load-bearing for the roadmap's central claim; no uniqueness theorem or forced conclusion is imported from these works. The paper even explicitly acknowledges unresolved enabling assumptions in Section 3.3, stating LLM agents 'are probabilistic in nature, higher-latency, and resource intensive compared to traditional methods, and are difficult to verify' and that 'there are no guarantees whether the solutions driven by these systems would be grounded in scientific knowledge and physics.' Those admissions weaken the paper's evidentiary strength but do not constitute circularity. Because no step reduces, by the paper's own text, to its inputs, the appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 1 invented entities

The roadmap rests on several unproven assumptions about technical standardization, AI reliability, and the achievability of dramatic speedups. It introduces AISLE as a new organizational entity with no independent measured evidence.

assumptions (4)

domain assumption Interoperability standards for scientific instruments can be developed across vendors and institutions.
Section 3.1 argues for vendor-agnostic hardware abstraction layers; this is a major technical premise on which cross-institutional orchestration rests.
domain assumption AI agents, including LLM-based agents, can achieve sufficient reliability and scientific grounding for autonomous experiment orchestration.
Section 3.3 lists challenges but the roadmap presumes verification can be built; no evidence given.
ad hoc to paper The 'decades to months' acceleration is a realizable outcome of interconnection.
Repeated in abstract and §1; treated as a goal, not derived.
domain assumption Non-technical barriers such as IP, liability, and institutional trust can be overcome.
Mentioned as 'critical organizational barriers' in §3.1 but not addressed with a plan.

invented entities (1)

AISLE (Autonomous Interconnected Science Lab Ecosystem)
purpose: Grassroots network to interconnect autonomous labs
Described as a website (autonomousscience.org) but no measured outcomes; no falsifiable handle beyond the proposed milestones.

how reviews work

0 comments

Cite this review

Pith. "Pith review of A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery." pith.science (2026). https://pith.science/paper/N4SC24TM

@misc{pith2026250617510,
  author       = {Pith},
  title        = {Pith review of: A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/N4SC24TM}},
  note         = {Machine review of arXiv:2506.17510}
}

read the original abstract

Scientific discovery is being revolutionized by AI and autonomous systems, yet current autonomous laboratories remain isolated islands unable to collaborate across institutions. We present the Autonomous Interconnected Science Lab Ecosystem (AISLE), a grassroots network transforming fragmented capabilities into a unified system that shorten the path from ideation to innovation to impact and accelerates discovery from decades to months. AISLE addresses five critical dimensions: (1) cross-institutional equipment orchestration, (2) intelligent data management with FAIR compliance, (3) AI-agent driven orchestration grounded in scientific principles, (4) interoperable agent communication interfaces, and (5) AI/ML-integrated scientific education. By connecting autonomous agents across institutional boundaries, autonomous science can unlock research spaces inaccessible to traditional approaches while democratizing cutting-edge technologies. This paradigm shift toward collaborative autonomous science promises breakthroughs in sustainable energy, materials development, and public health.

Figures

Figures reproduced from arXiv: 2506.17510 by the authors.

**Figure 1.** The AISLE network architecture illustrating the five critical dimensions for interconnected autonomous laboratories: [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Learning User Interaction Forces using Vision for a Soft Finger Exosuit
cs.RO 2025-08 unverdicted novelty 5.0 of 10

A machine-learning model trained on simulated images estimates contact forces on a soft finger exosuit from low-resolution grayscale images, and the authors show it can work as a force sensor for closed-loop control.

Reference graph

Works this paper leans on

36 extracted references · 32 canonical work pages · cited by 1 Pith paper

[1]

Al-Najjar et al

A. Al-Najjar et al. 2024. Autonomous electrochemistry platform with real-time normality testing of voltammetry measurements using ML. InIEEE 20th Interna- tional Conference on e-Science

work page 2024
[2]

Adaptive Tunability for Synthesis and Control via Autonomous Learning on Edge

AT SCALE 2025. Adaptive Tunability for Synthesis and Control via Autonomous Learning on Edge. https://www.pnnl.gov/projects/at-scale

work page 2025
[3]

https://www.anl.gov/autonomous-discovery

Autonomous Discovery 2025. https://www.anl.gov/autonomous-discovery

work page 2025
[4]

Bergamasco et al

A. Bergamasco et al . 2012. Knowledge discovery in large model datasets in the marine environment: The THREDDS Data Server example.Advances in Oceanography and Limnology3, 1 (2012)

work page 2012
[5]

Bran et al

A. Bran et al. 2024. Augmenting large language models with chemistry tools. Nature Machine Intelligence6, 5 (2024)

work page 2024
[6]

Chard et al

R. Chard et al. 2023. Globus automation services: Research process automation across the space–time continuum.FGCS142 (2023), 393–409

work page 2023
[7]

Ferreira da Silva et al

R. Ferreira da Silva et al. 2024.Shaping the Future of Self-Driving Autonomous Lab- oratories Workshop. Technical Report ORNL/TM-2024/3714. Oak Ridge National Laboratory. doi:10.5281/zenodo.14430233

doi:10.5281/zenodo.14430233 2024
[8]

Gridach et al

M. Gridach et al. 2025. Agentic AI for scientific discovery: A survey of progress, challenges, and future directions.arXiv:2503.08979(2025)

arXiv 2025

Show all 36 references

[9]

Hagos et al

D. Hagos et al. 2024. AI-Driven Human-Autonomy Teaming in Tactical Opera- tions: Proposed Framework, Challenges, and Future Directions.arXiv:2411.09788 (2024)

2024 arXiv
[10]

Enhancing the software ecosystem with LLMs

hpc-llm2025 2025. Enhancing the software ecosystem with LLMs. https:// computing.llnl.gov/about/newsroom/enhancing-software-ecosystem-llms

2025
[11]

Hysmith et al

H. Hysmith et al. 2024. The future of self-driving laboratories: From human in the loop interactive AI to gamification.Digital Discovery3, 4 (2024)

2024
[12]

Interconnected Science Ecosystem

INTERSECT 2025. Interconnected Science Ecosystem. https://www.ornl.gov/ intersect

2025
[13]

Lebo et al

T. Lebo et al. 2013. PROV-O: The PROV ontology. https://www.w3.org/TR/prov-o

2013
[14]

Luettgau et al

J. Luettgau et al. 2023. NSDF-services: Integrating networking, storage, and com- puting services into a testbed for democratization of data delivery. InIEEE/ACM 16th Intl Conf. on Utility and Cloud Computing

2023
[15]

The Modular Autonomous Discovery for Science (MADSci) Frame- work

MADSci 2025. The Modular Autonomous Discovery for Science (MADSci) Frame- work. https://github.com/AD-SDL/MADSci

2025
[16]

B. Mintz. 2023. Towards a Software Development Framework for Interconnected Science Ecosystems. In22nd Smoky Mountains Computational Sciences and Engi- neering Conference, SMC 2022

2023
[17]

Parashar and I

M. Parashar and I. Altintas. 2023. Toward democratizing access to science data: Introducing the National Data Platform. InIEEE 19th Intl. Conf. on e-Science

2023
[18]

Pauloski et al

G. Pauloski et al. 2024. Accelerating Python applications with Dask and Proxys- tore.arXiv:2410.12092(2024)

2024 arXiv
[19]

Pauloski et al

G. Pauloski et al. 2025. Empowering Scientific Workflows with Federated Agents. arXiv:2505.05428(2025)

2025
[20]

Profanter et al

S. Profanter et al. 2019. OPC UA versus ROS, DDS, and MQTT: Performance evaluation of industry 4.0 protocols. InIEEE Intl. Conf. on Industrial Technology

2019
[21]

Ravi et al

N. Ravi et al. 2022. FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy.Scientific Data9, 1 (2022)

2022
[22]

Ren et al

F. Ren et al. 2018. Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments.Science Advances4, 4 (2018)

2018
[23]

Sadeghi et al

S. Sadeghi et al. 2024. Engineering a Sustainable Future: Harnessing Automa- tion, Robotics, and Artificial Intelligence with Self-Driving Laboratories.ACS Sustainable Chemistry & Engineering12, 34 (2024)

2024
[24]

Sadeghi et al

S. Sadeghi et al . 2025. A self-driving fluidic lab for data-driven synthesis of lead-free perovskite nanocrystals.Digital Discovery(2025)

2025
[25]

Seifrid et al

M. Seifrid et al. 2022. Autonomous chemical experiments: Challenges and per- spectives on establishing a self-driving lab.Accounts of Chemical Research55, 17 (2022)

2022
[26]

Sim et al

M. Sim et al . 2024. ChemOS 2.0: An orchestration architecture for chemical self-driving laboratories.Matter7, 9 (2024)

2024
[27]

Singh et al

A. Singh et al. 2024.A Perspective on Decentralizing AI. Technical Report. MIT Media Lab

2024
[28]

P Stier et al

S. P Stier et al . 2024. Materials Acceleration Platforms (MAPs): Accelerating materials research and development to meet urgent societal challenges.Advanced Materials36, 45 (2024)

2024
[29]

Sumpter et al

B. Sumpter et al. 2023. Autonomous continuous flow reactor synthesis for scalable atom-precision.Carbon Trends10 (2023)

2023
[30]

Szymanski et al

N. Szymanski et al. 2023. An autonomous laboratory for the accelerated synthesis of novel materials.Nature624, 7990 (2023)

2023
[31]

Velankar et al

S. Velankar et al. 2021. The Protein Data Bank archive.Structural Proteomics: High-Throughput Methods(2021)

2021
[32]

Vescovi et al

R. Vescovi et al. 2022. Linking Instruments and Computation: Patterns, Technolo- gies, Experiences.Patterns3, 10 (2022)

2022
[33]

Wang et al

C. Wang et al. 2025. Autonomous platform for solution processing of electronic polymers.Nature Communications16 (2025), 1498

2025
[34]

Wilkinson et al

M. Wilkinson et al. 2016. The FAIR Guiding Principles for scientific data man- agement and stewardship.Scientific data3, 1 (2016)

2016
[35]

Xiao et al

Y. Xiao et al. 2024. CellAgent: An LLM-driven multi-agent framework for auto- mated single-cell data analysis.arXiv:2407.09811(2024)

2024 arXiv
[36]

Min Xu et al. 2018. The Fourth Industrial Revolution: Opportunities and Chal- lenges.International J. Financial Research9, 2 (2018)

2018

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

Al-Najjar et al

A. Al-Najjar et al. 2024. Autonomous electrochemistry platform with real-time normality testing of voltammetry measurements using ML. InIEEE 20th Interna- tional Conference on e-Science

work page 2024

[2] [2]

Adaptive Tunability for Synthesis and Control via Autonomous Learning on Edge

AT SCALE 2025. Adaptive Tunability for Synthesis and Control via Autonomous Learning on Edge. https://www.pnnl.gov/projects/at-scale

work page 2025

[3] [3]

https://www.anl.gov/autonomous-discovery

Autonomous Discovery 2025. https://www.anl.gov/autonomous-discovery

work page 2025

[4] [4]

Bergamasco et al

A. Bergamasco et al . 2012. Knowledge discovery in large model datasets in the marine environment: The THREDDS Data Server example.Advances in Oceanography and Limnology3, 1 (2012)

work page 2012

[5] [5]

Bran et al

A. Bran et al. 2024. Augmenting large language models with chemistry tools. Nature Machine Intelligence6, 5 (2024)

work page 2024

[6] [6]

Chard et al

R. Chard et al. 2023. Globus automation services: Research process automation across the space–time continuum.FGCS142 (2023), 393–409

work page 2023

[7] [7]

Ferreira da Silva et al

R. Ferreira da Silva et al. 2024.Shaping the Future of Self-Driving Autonomous Lab- oratories Workshop. Technical Report ORNL/TM-2024/3714. Oak Ridge National Laboratory. doi:10.5281/zenodo.14430233

doi:10.5281/zenodo.14430233 2024

[8] [8]

Gridach et al

M. Gridach et al. 2025. Agentic AI for scientific discovery: A survey of progress, challenges, and future directions.arXiv:2503.08979(2025)

arXiv 2025