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来自北卡罗来纳州立大学和布法罗大学的研究人员已经开发并展示了一种“自动驾驶实验室”，它使用人工智能（AI）和流体系统来推进我们对金属卤化物钙钛矿（MHP）纳米晶体的理解。这种自动驾驶实验室还可用于研究广泛的其他半导体和金属纳米材料。
“We’ve created a self-driving laboratory that can be used to advance both fundamental nanoscience and applied engineering,” says Milad Abolhasani, corresponding author of a paper on the work and an associate professor of chemical and bimolecular engineering at NC State.
对于他们的概念证据示范，研究人员集中在全无机金属卤化物钙钛矿（MHP）纳米晶体中，卤化铯（CSPBX）3., X=Cl, Br). MHP nanocrystals are an emerging class of semiconductor materials that, because of their solution-processability and unique size- and composition-tunable properties, are thought to have potential for use in printed photonic devices and energy technologies. For example, MHP nanocrystals are very efficient optically active materials and are under consideration for use in next-generation LEDs. And because they can be made using solution processing, they have the potential to be made in a cost-effective way.
溶液加工的材料是使用液体化学前体制造的材料，包括高价值材料，例如量子点，金属/金属氧化物纳米粒子和金属有机框架。
However, MHP nanocrystals are not in industrial use yet.
“部分是，这是因为我们仍然更好地了解如何合成这些纳米晶体以便工程师与MHPS相关的所有属性，”Abolhasani说。“并且部分原因是合成它们需要一定程度的精确度，这阻止了大规模的制造成本效益。我们的工作解决了这些问题。“
新技术扩展了概念人工化学家2.0, which Abolhasani’s lab unveiled in 2020. Artificial Chemist 2.0 is completely autonomous, and uses AI and automated robotic systems to perform multi-step chemical synthesis and analysis. In practice, that system focused on tuning the bandgap of MHP quantum dots, allowing users to go from requesting a custom quantum dot to completing the relevant R&D and beginning manufacturing in less than an hour.
“Our new self-driving lab technology can autonomously dope MHP nanocrystals, adding manganese atoms into the crystalline lattice of the nanocrystals on demand,” Abolhasani says.
掺杂具有不同水平的锰的材料改变纳米晶体的光学和电子性质，并将磁性引入材料上。例如，用锰掺杂MHP纳米晶体可以改变材料发出的光的波长。
“这种能力给了我们更大的控制关爱r the properties of the MHP nanocrystals,” Abolhasani says. “In essence, the universe of potential colors that can be produced by MHP nanocrystals is now larger. And it’s not just color. It offers a much greater range of electronic and magnetic properties.”
新的自动驾驶实验室技术还提供了更快，更高效的方法，了解如何工程师纳米晶间以获得所需的性能组合。可以找到新技术的视频https://www.youtube.com/watch?v=2bflpw6r4hi.。
“Let’s say you want to get an in-depth understanding of how manganese-doping and bandgap tuning will affect a specific class of MHP nanocrystals, such as CsPbX3.,” Abolhasani says. “There are approximately 160billion如果您想控制每个实验中的每种可能变量，可以运行的可能实验。使用常规技术，仍然需要数百或数千个实验，以了解这两个过程 - 掺杂和带隙调谐 - 会影响铯卤化铯纳米晶体的性质。“
但是新系统自主。Specifically, its AI algorithm selects and runs its own experiments. The results from each completed experiment inform which experiment it will run next – and it keeps going until it understands which mechanisms control the MHP’s various properties.
“We found, in a practical demonstration, that the system was able to get a thorough understanding of how these processes alter the properties of cesium lead halide nanocrystals in only 60 experiments,” Abolhasani says. “In other words, we can get the information we need to engineer a material in hours instead of months.”
虽然本文中所示的工作侧重于MHP纳米晶体，但是自主系统也可用于表征使用溶液过程制造的其他纳米材料，包括各种各样的金属和半导体纳米材料。
“We’re excited about how this technology will broaden our understanding of how to control the properties of these materials, but it’s worth noting that this system can also be used for continuous manufacturing,” Abolhasani says. “So you can use the system to identify the best possible process for creating your desired nanocrystals, and then set the system to start producing material nonstop – and with incredible specificity.
“We’ve created a powerful technology. And we’re now looking for partners to help us apply this technology to specific challenges in the industrial sector.”
本文，“Autonomous Nanocrystal Doping by Self-Driving Fluidic Micro-Processors,” is published open access in the journalAdvanced Intelligent Systems。The paper was co-authored by Fazel Bateni, a Ph.D. student at NC State; Robert Epps and Jeffery Bennett, postdoctoral researchers at NC State; Kameel Antami, a former Ph.D. student at NC State; Rokas Dargis, an undergraduate at NC State; and Kristofer Reyes, an assistant professor at the University at Buffalo.
The work was done with support from the National Science Foundation, under grant number 1940959, and from the UNC Research Opportunities Initiative.
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编辑注：The study abstract follows.
“Autonomous Nanocrystal Doping by Self-Driving Fluidic Micro-Processors”
Authors：Fazel Bateni，Robert W. EPPS，Kameel Antami，Rokas Dargis，Jeffery A. Bennett和Milad Abolhasani，北卡罗来纳州立大学;克里斯托夫·雷亚斯，大学在布法罗
Published: March 13, 2022 inAdvanced Intelligent Systems
迪伊: 10.1002/aisy.202200017
Abstract:卤化铅钙钙钛矿（LHP）纳米晶体（NCS）被认为是具有许多未出色的光电特性的先进功能材料的新出现类。尽管他们在该领域取得了成功，但他们的精确综合和基础机制研究仍然是一个挑战。LHP NCS的巨大胶体合成和加工参数与批量 - 批次和实验室到实验室变异问题相结合，进一步使其进展复杂化。作为响应，通过具有多级化学物质的NCS的复杂合成和处理参数空间，提出了一种自动化流体微处理器以加速导航。通过LHP NCS的顺序卤化物交换和阳离子掺杂反应，证明了发达的自主实验策略的能力。接下来，模块化流体微处理器的机器学习模型是自主建立的，用于加速LHP NC的流动金属阳离子掺杂的基本研究。然后利用LHP NCS的顺序卤化物交换和阳离子掺杂反应的替代模型，用于具有不同靶NC掺杂水平的五个闭环合成运动。本文提出的精确和智能的NC合成和加工策略可以进一步朝着自主发现和开发新的杂质掺杂NCS，其具有下一代能量技术的应用。
