ReviewNature communications2024
Performance metrics to unleash the power of self-driving labs in chemistry and materials science.
Review in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed.
- Integration of Artificial Intelligence and Microfluidics for Drug Delivery Applications.Micromachines · 2026Review
- Illuminating the Interface: In Situ Optical Insights Into Two-Electron ORR Pathways for HAdvanced materials (Deerfield Beach, Fla.) · 2026Review
- The past, present and future of self-driving laboratories.Nature reviews. Chemistry · 2026Review
- An autonomous lab for data-driven homogeneous catalysis.Nature communications · 2026Article
- A chemically-aware validation framework for benchmarking large language models in materials synthesis planning.Journal of cheminformatics · 2026Article
- Sustainable Materials Design With Multi-Modal Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Bridging electron microscopy and materials analysis with an autonomous agentic platform.Science advances · 2026Article
- Accelerated drug development using a digital formulator and a self-driving tableting data factory.Nature communications · 2026Article
- Article
- The ADePT framework for assessing autonomous laboratory robotics.Communications chemistry · 2026Review
- What are the limits to biomedical research acceleration through general-purpose AI?Scientific reports · 2026Review
- Programmable Light-Driven Color Tuning of Perovskite Quantum Dots.ACS central science · 2025Article
- Self-driving lab discovers principles for steering spontaneous emission beyond conventional Fourier optics.Nature communications · 2025Article
- Evaluating large language model agents for automation of atomic force microscopy.Nature communications · 2025Article
- 33 Unresolved Questions in Nanoscience and Nanotechnology.ACS nano · 2025Article
- Autonomous multi-robot synthesis and optimization of metal halide perovskite nanocrystals.Nature communications · 2025Article
- Efficient discovery of new medicine formulations using a semi-self-driven robotic formulator.Digital discovery · 2025Article
- Science acceleration and accessibility with self-driving labs.Nature communications · 2025Review
- Self-driving lab for the photochemical synthesis of plasmonic nanoparticles with targeted structural and optical properties.Nature communications · 2025Article
- From Sunlight to Solutions: Closing the Loop on Halide Perovskites.ACS materials Au · 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
2 authors.
Funding
Abstract
With the rise of self-driving labs (SDLs) and automated experimentation across chemical and materials sciences, there is a considerable challenge in designing the best autonomous lab for a given problem based on published studies alone. Determining what digital and physical features are germane to a specific study is a critical aspect of SDL design that needs to be approached quantitatively. Even when controlling for features such as dimensionality, every experimental space has unique requirements and challenges that influence the design of the optimal physical platform and algorithm. Metrics such as optimization rate are therefore not necessarily indicative of the capabilities of an SDL across different studies. In this perspective, we highlight some of the critical metrics for quantifying performance in SDLs to better guide researchers in implementing the most suitable strategies. We then provide a brief review of the existing literature under the lens of quantified performance as well as heuristic recommendations for platform and experimental space pairings.
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.