ReviewChemical reviews2024
Self-Driving Laboratories for Chemistry and Materials Science.
Review in Chemical reviews, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 144 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
144 citing papers in PubMed.
- AI-Driven Synthesis in Medicinal Chemistry: Integrating Large Language Models, Robotic Automation, and Sustainability Metrics to Accelerate Drug Discovery.Medicinal research reviews · 2026Review
- A Practical Guide to Bayesian Optimization for Organic Chemists.Chem catalysis · 2026Article
- AI-driven electrocatalysis: reinforcement learning-based control and theoretical modeling of the glycerol oxidation reaction.Energy & environmental science · 2026Article
- Chemistry-informed Bayesian control and policy distillation for autonomous pH adjustment in diverse chemical systems.Chemical science · 2026Article
- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
- Surface Ligands in Nanocrystal Electrocatalysis: A Double-Edged Sword.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Envisioning AI-empowered ecological, environmental, and health protection: Welcome submissions of interdisciplinary research.Eco-Environment & Health · 2026Article
- Artifact segmentation using the U-Net architecture for powder X-ray diffraction images.Journal of synchrotron radiation · 2026Article
- AI-driven robotics for optics.Science advances · 2026Article
- Three privileged scaffolds, one pipeline: chalcones, pyrazolines and pyrazoles in drug discovery and fluorescent sensing.RSC medicinal chemistry · 2026Review
- Transforming Molecular Synthesis With Large Language Models.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Review
- Self-Driving Scanning Probe Microscopy: From Acceleration to Discovery and Manipulation.Accounts of chemical research · 2026Article
- Machine-Learning-Driven Optimization of Functional Excipients and Their Biointeractions in Drug Formulations.ACS pharmacology & translational science · 2026Review
- Interpretable self-driving sputtering epitaxy reveals human-usable growth rules for β-GaNature communications · 2026Article
- AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization.Chemical reviews · 2026Article
- Mentorship Strategies for New Principal Investigators in Computational Chemistry.Journal of chemical information and modeling · 2026Article
- Automating Chemical Reasoning in High-Throughput Phase Identification With a Probabilistic, LLM-Guided Framework.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Rank-guided learning accelerates automated enzyme engineering.Nature communications · 2026Article
- Artificial intelligence in contact sports research: Promise and peril for predicting and preventing brain injury.Experimental physiology · 2026Article
- Diagnostic and therapeutic potential of carbon nanodots for ocular disease.Nanomedicine (London, England) · 2026Review
84 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.
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.