ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2020
Toward "On-Demand" Materials Synthesis and Scientific Discovery through Intelligent Robots.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Rational Design of Metal Oxide Nanostructures via Dopant Control: A Case Study in Photoelectrochemical Performance.ACS applied materials & interfaces · 2025Article
- Optimized Machine Learning for Autonomous Enzymatic Reaction Intensification in a Self-Driving Lab.Biotechnology and bioengineering · 2025Article
- Light-Controlled Adhesive Hydrogels for On-Demand Adhesion.Chem & bio engineering · 2025Article
- Self-driving lab for the photochemical synthesis of plasmonic nanoparticles with targeted structural and optical properties.Nature communications · 2025Article
- Continuous Flow Synthesis of Copper Oxide Nanoparticles Enabling Rapid Screening of Synthesis-Structure-Property Relationships.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- OCTOPUS: operation control system for task optimization and job parallelization via a user-optimal scheduler.Nature communications · 2024Article
- Materials descriptors of machine learning to boost development of lithium-ion batteries.Nano convergence · 2024Review
- Toward the Uniform of Chemical Theory, Simulation, and Experiments in Metaverse Technology.Precision chemistry · 2023Review
- Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Review
- Artificial Intelligence Applied to Battery Research: Hype or Reality?Chemical reviews · 2022Review
- Review
- Data-Driven Methods for Accelerating Polymer Design.ACS polymers Au · 2022Review
- Designing a multilayer film via machine learning of scientific literature.Scientific reports · 2022Article
- Functional and Material Properties in Nanocatalyst Design: A Data Handling and Sharing Problem.International journal of molecular sciences · 2021Review
- Autonomous discovery of optically active chiral inorganic perovskite nanocrystals through an intelligent cloud lab.Nature communications · 2020Article
- Toward "On-Demand" Materials Synthesis and Scientific Discovery through Intelligent Robots.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A Materials Acceleration Operation System (MAOS) is designed, with unique language and compiler architecture. MAOS integrates with virtual reality (VR), collaborative robots, and a reinforcement learning (RL) scheme for autonomous materials synthesis, properties investigations, and self-optimized quality assurance. After training through VR, MAOS can work independently for labor and intensively reduces the time cost. Under the RL framework, MAOS also inspires the improved nucleation theory, and feedback for the optimal strategy, which can satisfy the demand on both of the CdSe quantum dots (QDs) emission wavelength and size distribution quality. Moreover, it can work well for extensive coverages of inorganic nanomaterials. MAOS frees the experimental researchers out of the tedious labor as well as the extensive exploration of optimal reaction conditions. This work provides a walking example for the "On-Demand" materials synthesis system, and demonstrates how artificial intelligence technology can reshape traditional materials science research in the future.
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Registered trials
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