材料加速平台的演变--与AMANDA一起走向未来的实验室
The Evolution of Materials Acceleration Platforms -- Towards the Laboratory of the Future with AMANDA
摘要：
复杂功能材料的开发提出了一个大的多维参数空间中的多目标优化问题。解决这个问题需要可重复的、用户独立的实验室工作和智能预选实验。然而，实验材料科学是一个手动例程仍然占主导地位的领域，尽管其他领域如制药或化学长期使用机器人和自动化。随着材料加速平台（MAPs）的出版物数量稳步增加，我们回顾了选定的系统，并将其融入一般材料开发过程的各个阶段，以研究MAPs的演变。随后，我们提出了我们的方法，在材料科学实验室自动化。我们介绍AMANDA（自主材料和设备应用平台），一个通用的分布式材料研究平台，包括一个自主开发的软件骨干和几个地图。其中之一LineOne（L1）专门用于生产和表征溶液处理的薄膜器件，如有机太阳能电池（OSC）。它旨在每天对多达272种设备变体进行精确的闭环筛选，并允许进一步升级。每一块太阳能电池都经过充分表征，所有工艺步骤都有全面记录。我们希望展示AMANDA L1与基于PM 6：Y 6的OSC在空气中处理时具有13.7%效率的能力。此外，我们还讨论了高度自动化研究平台的挑战和机遇，并详细阐述了未来其他技术，方法和算法的整合，以推进到完全自主的自我优化系统-功能材料开发的范式转变，导致未来的实验室。
英文摘要：
The development of complex functional materials poses a multi-objective optimization problem in a large multidimensional parameter space. Solving it requires reproducible, user independent laboratory work and intelligent preselection of experiments. However, experimental materials science is a field where manual routines are still predominant, although other domains like pharmacy or chemistry have long used robotics and automation. As the number of publications on Materials Acceleration Platforms (MAPs) increases steadily, we review selected systems and fit them into the stages of a general material development process to examine the evolution of MAPs. Subsequently we present our approach to laboratory automation in materials science. We introduce AMANDA (Autonomous Materials and Device Application Platform), a generic platform for distributed materials research comprising a self-developed software backbone and several MAPs. One of them, LineOne (L1), is specifically designed to produce and characterize solution processed thin-film devices like organic solar cells (OSC). It is designed to perform precise closed-loop screenings of up to 272 device variations per day yet allows further upscaling. Each individual solar cell is fully characterized and all process steps are comprehensively documented. We want to demonstrate the capabilities of AMANDA L1 with OSCs based on PM6:Y6 with 13.7% efficiency when processed in air. Further we discuss challenges and opportunities of highly automated research platforms and elaborate on the future integration of additional techniques, methods and algorithms in order to advance to fully autonomous self-optimizing systems - a paradigm shift in functional materials development leading to the laboratory of the future.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
