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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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7141037 | PMC |
| http://dx.doi.org/10.1002/advs.201901957 | DOI Listing |
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