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

2025-12-17

AI Summary

Controlling the high-density, high-quality growth of semiconducting carbon nanotubes (s-CNTs) remains a fundamental challenge due to the lack of rational catalyst design strategies. Method: This work establishes a knowledge- and data-driven integrated AI framework for end-to-end catalyst design, departing from conventional trial-and-error approaches. It introduces a novel photo-regulated catalyst-mediated electron injection mechanism and proposes a cross-scale design paradigm synergizing curated databases, NLP-based chemical embedding, and physics-informed predictive models—ensuring both interpretability and strong generalizability. The framework integrates a first-principles electronic structure database, a pre-trained NLP embedding model, and an experimentally calibrated physical model, validated via high-throughput experiments. Results: Among 54 candidate catalysts, three high-potential systems were identified; experimental validation achieved s-CNT selectivity exceeding 91%, with FeTiO₃ reaching 98.6%—marking a substantial improvement over state-of-the-art methods.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Constraint Satisfaction and Optimization: Satisfiability
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Designing catalysts for high-quality semiconducting carbon nanotube synthesis
• Integrating machine learning with traditional catalyst design methods
• Screening and validating catalysts to achieve high semiconducting selectivity

Innovation

• Integrating machine learning with traditional catalyst design
• Using NLP and electronic databases for catalyst descriptors
• Proposing light-tuned catalyst-mediated electron injection method