利用机器学习模型与自动化变革碳纳米管的合成
Transforming the Synthesis of Carbon Nanotubes with Machine Learning Models and Automation
AI总结：
本文提出AI驱动的Carbon Copilot平台，融合语言模型、机器人CVD和机器学习，在43天内通过预测钛-铂催化剂和数百万虚拟实验，实现高密度碳纳米管阵列合成精度56.25%。
摘要：
碳基纳米材料（CBNs）在电子、能源和力学等多个领域展现出巨大潜力。然而，其实际应用面临合成挑战，这些挑战源于结构控制、大面积均匀性和高产率方面的复杂性。当前的研究方法难以应对CBNs生产中固有的多变量耦合相互作用。机器学习方法擅长驾驭此类复杂性。它们与自动化合成平台的集成在加速化学合成研究方面已展现出显著潜力，但在纳米材料领域仍未得到充分探索。在此，我们介绍Carbon Copilot（CARCO），这是一个由人工智能（AI）驱动的平台，集成了为碳材料量身定制的基于Transformer的语言模型、机器人化学气相沉积（CVD）以及数据驱动的机器学习模型，从而赋能CBNs合成的加速研究。利用CARCO，我们展示了创新的催化剂发现，通过预测一种优异的钛-铂双金属催化剂，用于高密度水平排列碳纳米管（HACNT）阵列合成，并通过500多次实验得到验证。此外，在数百万次虚拟实验的辅助下，我们在现实世界中以预定密度合成HACNT阵列时实现了前所未有的56.25%的精度。所有这些仅在43天内完成。这项工作不仅推动了HACNT阵列领域的发展，还例证了人工智能与人类专业知识相结合以克服传统实验方法局限性的方式，标志着纳米材料研究的范式转变，并为更广泛的应用铺平了道路。
英文摘要：
Carbon-based nanomaterials (CBNs) are showing significant potential in various fields, such as electronics, energy, and mechanics. However, their practical applications face synthesis challenges stemming from the complexities of structural control, large-area uniformity, and high yield. Current research methodologies fall short in addressing the multi-variable, coupled interactions inherent to CBNs production. Machine learning methods excel at navigating such complexities. Their integration with automated synthesis platforms has demonstrated remarkable potential in accelerating chemical synthesis research, but remains underexplored in the nanomaterial domain. Here we introduce Carbon Copilot (CARCO), an artificial intelligence (AI)-driven platform that integrates transformer-based language models tailored for carbon materials, robotic chemical vapor deposition (CVD), and data-driven machine learning models, empowering accelerated research of CBNs synthesis. Employing CARCO, we demonstrate innovative catalyst discovery by predicting a superior Titanium-Platinum bimetallic catalyst for high-density horizontally aligned carbon nanotube (HACNT) array synthesis, validated through over 500 experiments. Furthermore, with the assistance of millions of virtual experiments, we achieved an unprecedented 56.25% precision in synthesizing HACNT arrays with predetermined densities in the real world. All were accomplished within just 43 days. This work not only advances the field of HACNT arrays but also exemplifies the integration of AI with human expertise to overcome the limitations of traditional experimental approaches, marking a paradigm shift in nanomaterials research and paving the way for broader applications.
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。
