ArticlePloS one2020
ChemOS: An orchestration software to democratize autonomous discovery.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed, 139 citations in OpenAlex.
- Digital infrastructure to support and share daily high-throughput experimental materials research.Faraday discussions · 2026Article
- Automated Biomedical Research Laboratories: Development, Current State, and a Roadmap for Adaptation into Shared Research Resources.Journal of biomolecular techniques : JBT · 2026Review
- Can automated gold nanoparticle synthesis drive the next wave of biomedical innovation?Nanomedicine (London, England) · 2025Article
- Real-time experiment-theory closed-loop interaction for autonomous materials science.Science advances · 2025Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Autonomous 'self-driving' laboratories: a review of technology and policy implications.Royal Society open science · 2025Review
- Autonomous Small-Angle Scattering for Accelerated Soft Material Formulation Optimization.Chemistry of materials : a publication of the American Chemical Society · 2025Article
- A Vision for the Future of Materials Innovation and How to Fast-Track It with Services.ACS physical chemistry Au · 2024Review
- Self-Driving Laboratories for Chemistry and Materials Science.Chemical reviews · 2024Review
- Data-driven development of an oral lipid-based nanoparticle formulation of a hydrophobic drug.Drug delivery and translational research · 2024Article
- The advancement of artificial intelligence in biomedical research and health innovation: challenges and opportunities in emerging economies.Globalization and health · 2024Review
- How the AI-assisted discovery and synthesis of a ternary oxide highlights capability gaps in materials science.Chemical science · 2024Article
- Accelerated chemical science with AI.Digital discovery · 2024Review
- Rapid planning and analysis of high-throughput experiment arrays for reaction discovery.Nature communications · 2023Article
- Toward the Uniform of Chemical Theory, Simulation, and Experiments in Metaverse Technology.Precision chemistry · 2023Review
- Emerging Trends in Machine Learning: A Polymer Perspective.ACS polymers Au · 2023Review
- Open-Source Chromatographic Data Analysis for Reaction Optimization and Screening.ACS central science · 2023Article
- Machine learning for a sustainable energy future.Nature reviews. Materials · 2023Review
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The current Edisonian approach to discovery requires up to two decades of fundamental and applied research for materials technologies to reach the market. Such a slow and capital-intensive turnaround calls for disruptive strategies to expedite innovation. Self-driving laboratories have the potential to provide the means to revolutionize experimentation by empowering automation with artificial intelligence to enable autonomous discovery. However, the lack of adequate software solutions significantly impedes the development of self-driving laboratories. In this paper, we make progress towards addressing this challenge, and we propose and develop an implementation of ChemOS; a portable, modular and versatile software package which supplies the structured layers necessary for the deployment and operation of self-driving laboratories. ChemOS facilitates the integration of automated equipment, and it enables remote control of automated laboratories. ChemOS can operate at various degrees of autonomy; from fully unsupervised experimentation to actively including inputs and feedbacks from researchers into the experimentation loop. The flexibility of ChemOS provides a broad range of functionality as demonstrated on five applications, which were executed on different automated equipment, highlighting various aspects of the software package.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.