Transforming the synthesis of carbon nanotubes with machine learning models and automation

Abstract

Carbon-based nanomaterials (CBNs) hold immense promise in electronics, energy, and mechanics. However, their practical applications face synthesis challenges stemming from complexities in structural control, large-area uniformity, and consistency, unaddressed by current research methodologies. Here, we introduce carbon copilot (CARCO), an artificial intelligence (AI)-driven platform that integrates transformer-based language models, robotic chemical vapor deposition (CVD), and data-driven machine learning models. Employing CARCO, we discovered a novel titanium-platinum bimetallic catalyst for high-density horizontally aligned carbon nanotube (HACNT) array synthesis, outperforming traditional catalysts. Furthermore, leveraging millions of virtual experiments, we achieved an unprecedented 56.25% precision in synthesizing predetermined densities of HACNT arrays. All were accomplished within just 43 days. This work not only advances the field of CBNs 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.