Artificial Intelligence-Enabled Holistic Design of Catalysts Tailored for Semiconducting Carbon Nanotube Growth

TL;DR

This paper introduces a holistic machine learning framework for designing catalysts that enable high-quality semiconducting carbon nanotube growth, combining data-driven models with experimental validation to achieve high selectivity.

Contribution

It presents a novel integrated approach combining electronic structure data, NLP embeddings, and physical models for catalyst design, specifically targeting semiconducting CNT synthesis.

Findings

High semiconducting selectivity (>91%) achieved in experiments.

Identification of three promising catalysts, including FeTiO3 with 98.6% selectivity.

Validated the framework's effectiveness in catalyst screening and design.

Abstract

Catalyst design is crucial for materials synthesis, especially for complex reaction networks. Strategies like collaborative catalytic systems and multifunctional catalysts are effective but face challenges at the nanoscale. Carbon nanotube synthesis contains complicated nanoscale catalytic reactions, thus achieving high-density, high-quality semiconducting CNTs demands innovative catalyst design. In this work, we present a holistic framework integrating machine learning into traditional catalyst design for semiconducting CNT synthesis. It combines knowledge-based insights with data-driven techniques. Three key components, including open-access electronic structure databases for precise physicochemical descriptors, pre-trained natural language processing-based embedding model for higher-level abstractions, and physical - driven predictive models based on experiment data, are utilized. Through this framework, a new method for selective semiconducting CNT synthesis via catalyst - mediated electron injection, tuned by light during growth, is proposed. 54 candidate catalysts are screened, and three with high potential are identified. High-throughput experiments validate the predictions, with semiconducting selectivity exceeding 91% and the FeTiO3 catalyst reaching 98.6%. This approach not only addresses semiconducting CNT synthesis but also offers a generalizable methodology for global catalyst design and nanomaterials synthesis, advancing materials science in precise control.