信息物理系统支持的纳米晶体数字化制造:一个晶体

Haitao Zhao, Wei Chen, Zhuo Wang, Zhehao Sun, Chensu Wang, Fuming Lai, Hao Huang, O. A. Moses, M. Adam, Zijian Chen, Yichuan He, C. Pang, Yang Lu, P. K. Chu, Z. Yin, Xuefeng Yu
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引用次数: 0

摘要

材料数字化制造是一种合理的、可设计的战略,在宏观上越来越受欢迎。然而,在纳米尺度上控制纳米晶体的形态是一个挑战。在此,我们介绍Crystputer作为最先进的网络物理系统,它将通过网络和物理系统的融合实现纳米晶体的数字化制造。在网络系统中,设计了包括代码、工作文件、数学模型和数据库在内的合理设计的全可编程过程。在物理系统中,通过机器人辅助合成和纳米晶体生长的协同耦合来实现可控合成,这得益于在宏观尺度上确定的结构导向剂作为表面能的触发器来控制原子尺度上的形态。在Crystputer效率的驱动下,超过2300个实验与原位表征一起自主进行,以建立金纳米晶体基因组。进一步为晶体机设计了带有逻辑门的基因组结构。实验证明,利用机器学习预测和逻辑计算的优势,Crystputer可以被训练成一个有经验的反合成和放大合成目标金纳米棒的专家。设计-控制-合成-表征-计算-反合成的复杂机制使该晶体能够为目标金纳米晶体的合理设计,可控合成和反合成提供前所未有的性能和效率。这些见解和新知识为提高Crystputer在定制纳米晶体智能数字化制造方面的可行性提供了机会,以促进数据驱动的材料创新从“代码”到“纳米晶体”的范式转变。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cyber-Physical System Enabled Digital Manufacturing of Nanocrystals: A Crystputer
Digital manufacturing of materials in a rational and designable strategy and is gaining popularity on the macro-scale . However, it is challenging to control the nanocrystal morphologies on a nano-scale. Herein, we introduce Crystputer as a state-of-the-art cyber- physical system , which will enable digital manufacture of nanocrystals through convergence of cyber and physical systems. In the cyber system, an all-programmable process with rational design including codes, working files, mathematic models, and databases is designed. In the physical system, controllable synthesis is achieved by synergistic coupling of robot-assisted synthesis and nanocrystal growth , which are benefited from the identified structure-directing agents on the macro-scale as triggers of the surface energy to control the morphology on the atomic-scale. Driven by the Crystputer efficiency, over 2,300 experiments are conducted autonomously together with in situ characterization to build up the Au nanocrystals genome. The genome architecture with logic gates is further designed for the Crystputer. It is demonstrated that the Crystputer can be trained as an experienced expert for retrosynthesis and scale-up synthesis of targeted Au nanorods by taking advantage of the machine learning prediction and logic computation. The sophisticated mechanism encompassing design-control-synthesis-characterization-computing-retrosynthesis enables this Crystputer to deliver unprecedented performance and efficiency for rational design, controllable synthesis, and retrosynthesis of target Au nanocrystals. The insights and new knowledge open an opportunity to enhance the feasibility of Crystputer for intelligent digital manufacturing of customized nanocrystals in order to facilitate the paradigm shift of data-driven materials innovation from ‘code’ to ‘nanocrystals’.
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