ArticleScientific reports2020
Efficient Closed-loop Maximization of Carbon Nanotube Growth Rate using Bayesian Optimization.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 86 citations in OpenAlex.
- Accelerated Discovery of 3D Printing Calcium Sulphoaluminate Cement Composites Using Data-Driven Multi-Objective Optimization.Materials (Basel, Switzerland) · 2026Article
- Accelerated Emergence of Self-Driving Laboratories for Accelerating Materials Discovery.ACS central science · 2026Review
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- Real-time experiment-theory closed-loop interaction for autonomous materials science.Science advances · 2025Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Advancing Carbon Nanotube Fibers: Addressing Challenges from Production to Application.ACS central science · 2025Review
- Article
- Machine Learning as a "Catalyst" for Advancements in Carbon Nanotube Research.Nanomaterials (Basel, Switzerland) · 2024Review
- Article
- Self-Driving Laboratories for Chemistry and Materials Science.Chemical reviews · 2024Review
- Article
- Continuous flow synthesis of pyridinium salts accelerated by multi-objective Bayesian optimization with active learning.Chemical science · 2023Article
- Review
- An artificial intelligence enabled chemical synthesis robot for exploration and optimization of nanomaterials.Science advances · 2022Article
- Accelerated discovery of 3D printing materials using data-driven multiobjective optimization.Science advances · 2021Article
- A Meta-Analysis of Conductive and Strong Carbon Nanotube Materials.Advanced materials (Deerfield Beach, Fla.) · 2021Review
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A major technological challenge in materials research is the large and complex parameter space, which hinders experimental throughput and ultimately slows down development and implementation. In single-walled carbon nanotube (CNT) synthesis, for instance, the poor yield obtained from conventional catalysts is a result of limited understanding of input-to-output correlations. Autonomous closed-loop experimentation combined with advances in machine learning (ML) is uniquely suited for high-throughput research. Among the ML algorithms available, Bayesian optimization (BO) is especially apt for exploration and optimization within such high-dimensional and complex parameter space. BO is an adaptive sequential design algorithm for finding the global optimum of a black-box objective function with the fewest possible measurements. Here, we demonstrate a promising application of BO in CNT synthesis as an efficient and robust algorithm which can (1) improve the growth rate of CNT in the BO-planner experiments over the seed experiments up to a factor 8; (2) rapidly improve its predictive power (or learning); (3) Consistently achieve good performance regardless of the number or origin of seed experiments; (4) exploit a high-dimensional, complex parameter space, and (5) achieve the former 4 tasks in just over 100 hundred experiments (~8 experimental hours) - a factor of 5× faster than our previously reported results.
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Registered trials
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