Li Jiagen, Tu Yuxiao, Liu Rulin, Lu Yihua, Zhu Xi
Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS) The Chinese University of Hong Kong Shenzhen Guangdong 518172 China.
Adv Sci (Weinh). 2020 Feb 3;7(7):1901957. doi: 10.1002/advs.201901957. eCollection 2020 Apr.
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.
设计了一种材料加速操作系统（MAOS），它具有独特的语言和编译器架构。MAOS与虚拟现实（VR）、协作机器人以及用于自主材料合成、性能研究和自我优化质量保证的强化学习（RL）方案集成。通过VR训练后，MAOS可以独立工作，大大减少了时间成本。在RL框架下，MAOS还激发了改进的成核理论，并为最优策略提供反馈，这可以满足对CdSe量子点（QDs）发射波长和尺寸分布质量的要求。此外，它对于无机纳米材料的广泛覆盖也能很好地发挥作用。MAOS将实验研究人员从繁琐的劳动以及对最佳反应条件的广泛探索中解放出来。这项工作为“按需”材料合成系统提供了一个鲜活的例子，并展示了人工智能技术在未来如何重塑传统材料科学研究。
