ReviewNature communications2025
Science acceleration and accessibility with self-driving labs.
Review in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed.
- The rise of agentic AI in drug discovery from static tools to autonomous virtual scientists.npj drug discovery · 2026Review
- Data-Driven Materials Science for Energy-Sustainable Applications.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Data-driven construction of an imine reductase library capable of broad-scope reductive amination at equimolar substrate concentrations.Nature communications · 2026Article
- The past, present and future of self-driving laboratories.Nature reviews. Chemistry · 2026Review
- Opportunities for artificial intelligence and synthetic biology in designing living drug delivery systems.Advanced drug delivery reviews · 2026Review
- AutoLabs: cognitive multi-agent systems with self-correction for autonomous chemical experimentation.Scientific reports · 2026Article
- An autonomous lab for data-driven homogeneous catalysis.Nature communications · 2026Article
- Introduction to high throughput synthesis, characterisation and optimisation of nanomaterials.Nanoscale advances · 2026Article
- A chemically-aware validation framework for benchmarking large language models in materials synthesis planning.Journal of cheminformatics · 2026Article
- Adaptive, Bayesian Experimental Design to Efficiently Determine the Critical Micelle Concentration of a Surfactant.Langmuir : the ACS journal of surfaces and colloids · 2026Article
- Accelerated Emergence of Self-Driving Laboratories for Accelerating Materials Discovery.ACS central science · 2026Review
- Autonomous microfluidic labs: progress and prospects.Lab on a chip · 2026Review
- Harnessing artificial intelligence to advance CRISPR-based genome editing technologies.Nature reviews. Genetics · 2026Review
- Generative Models for Crystalline Materials.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- General-Purpose Models for the Chemical Sciences: LLMs and Beyond.Chemical reviews · 2026Review
- The ADePT framework for assessing autonomous laboratory robotics.Communications chemistry · 2026Review
- Beyond replacement anxiety: a psychological framework for understanding AI in natural science research.Frontiers in psychology · 2026Article
- From capability uplift to capability governance: an AI-biosecurity stack.Frontiers in microbiology · 2026Article
- A Visual Dataset for Anomaly Detection in Self-Driving Laboratories.Scientific data · 2025Article
- DNA methylation and machine learning: challenges and perspective toward enhanced clinical diagnostics.Clinical epigenetics · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
In the evolving landscape of scientific research, the complexity of global challenges demands innovative approaches to experimental planning and execution. Self-Driving Laboratories (SDLs) automate experimental tasks in chemical and materials sciences and the design and selection of experiments to optimize research processes and reduce material usage. This perspective explores improving access to SDLs via centralized facilities and distributed networks. We discuss the technical and collaborative challenges in realizing SDLs' potential to enhance human-machine and human-human collaboration, ultimately fostering a more inclusive research community and facilitating previously untenable research projects.
Indexed as
Identifiers
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.