ArticleNature communications2023
AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
What it found
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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.
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Who cites it
48 citing papers in PubMed.
- Microfluidic-assisted metal nanoparticle synthesis: emerging trends toward optical sensing applications.RSC advances · 2026Review
- Tunable Surface Chemistry of Molten Salt Derived MXenes for Data-Driven Electrochemical Materials Discovery.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- The past, present and future of self-driving laboratories.Nature reviews. Chemistry · 2026Review
- Uncertainty-Aware Machine Learning for Small-Angle X‑ray Scattering Analysis in Autonomous Experimentation.Photon science · 2026Article
- On-demand growth of semiconductor heterostructures guided by physics-informed machine learning.Science advances · 2026Article
- High-Throughput Methods in Materials Science (Part I): A Review of Chemical and Physical Methods and Automated Sample Logistics.Materials (Basel, Switzerland) · 2026Review
- What will be the future of computational biology for macromolecules in the era of AI?PLoS computational biology · 2026Article
- AutoLabs: cognitive multi-agent systems with self-correction for autonomous chemical experimentation.Scientific reports · 2026Article
- Toward Intelligent Sensing Systems: Non-Equilibrium Materials as Platforms for AI-Enabled Autonomous Discovery.Sensors (Basel, Switzerland) · 2026Article
- Enhancing multi-component alloy composition prediction based on generative adversarial networks and proximal policy optimization.Scientific reports · 2026Article
- Sustainable Materials Design With Multi-Modal Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Autonomous microfluidic labs: progress and prospects.Lab on a chip · 2026Review
- Review
- PredictingACS omega · 2026Article
- Artificial intelligence-driven approaches for materials design and discovery.Nature materials · 2026Review
- Accelerating primer design for amplicon sequencing using large language model-powered agents.Nature biomedical engineering · 2026Article
- Aqueous Continuous Flow Synthesis of Cadmium Chalcogenide Quantum Dots: Opportunities and Challenges.JACS Au · 2026Review
- High-throughput yeast engineering in biofoundries: towards autonomous and scalable synthetic biology.FEMS yeast research · 2026Review
- PhaseTransfer: A transfer learning framework for efficient phase diagram mapping.npj computational materials · 2026Article
- Recent Advances in Reactive Microdroplets for Clean Water and Energy.Advanced materials (Deerfield Beach, Fla.) · 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
7 authors.
Funding
Abstract
Closed-loop, autonomous experimentation enables accelerated and material-efficient exploration of large reaction spaces without the need for user intervention. However, autonomous exploration of advanced materials with complex, multi-step processes and data sparse environments remains a challenge. In this work, we present AlphaFlow, a self-driven fluidic lab capable of autonomous discovery of complex multi-step chemistries. AlphaFlow uses reinforcement learning integrated with a modular microdroplet reactor capable of performing reaction steps with variable sequence, phase separation, washing, and continuous in-situ spectral monitoring. To demonstrate the power of reinforcement learning toward high dimensionality multi-step chemistries, we use AlphaFlow to discover and optimize synthetic routes for shell-growth of core-shell semiconductor nanoparticles, inspired by colloidal atomic layer deposition (cALD). Without prior knowledge of conventional cALD parameters, AlphaFlow successfully identified and optimized a novel multi-step reaction route, with up to 40 parameters, that outperformed conventional sequences. Through this work, we demonstrate the capabilities of closed-loop, reinforcement learning-guided systems in exploring and solving challenges in multi-step nanoparticle syntheses, while relying solely on in-house generated data from a miniaturized microfluidic platform. Further application of AlphaFlow in multi-step chemistries beyond cALD can lead to accelerated fundamental knowledge generation as well as synthetic route discoveries and optimization.
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.