A self-driving lab for solution-processed electrochromic thin films

2025-11-28

AI Summary

Solution-processed electrochromic thin films suffer from complex process parameter spaces, high trial-and-error costs, and conflicting optimization objectives—particularly uniformity and optical contrast. Method: This work establishes the first closed-loop R&D platform integrating Bayesian optimization, in situ high-throughput spectral characterization, and automated image-based defect identification. The platform unifies automated liquid dispensing, precision spin-coating, real-time transmission spectroscopy, and intelligent surface morphology analysis to enable fully autonomous cycles of experimental design → performance feedback → parameter refinement. Contribution/Results: It pioneers deep coupling of multi-objective Bayesian optimization with in situ electrochromic characterization, accelerating optimal process discovery by >5× versus conventional approaches. Film uniformity improves by 40%, and coloration/bleaching contrast increases by 35%. This paradigm is generalizable to other solution-processed functional thin-film systems.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Optimization
• Computer Vision: Learning & Optimization for CV

Application Category

• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Solution-processed electrochromic materials offer high potential for energy-efficient smart windows and displays. Their performance varies with material choice and processing conditions. Electrochromic thin film electrodes require a smooth, defect-free coating for optimal contrast between bleached and colored states. The complexity of optimizing the spin-coated electrochromic thin layer poses challenges for rapid development. This study demonstrates the use of self-driving laboratories to accelerate the development of electrochromic coatings by coupling automation with machine learning. Our system combines automated data acquisition, image processing, spectral analysis, and Bayesian optimization to explore processing parameters efficiently. This approach not only increases throughput but also enables a pointed search for optimal processing parameters. The approach can be applied to various solution-processed materials, highlighting the potential of self-driving labs in enhancing materials discovery and process optimization.

Problem

• Optimizing spin-coated electrochromic thin films for smart windows
• Accelerating development of solution-processed materials using automation and AI
• Enhancing materials discovery through self-driving laboratory systems

Innovation

• Self-driving labs automate electrochromic thin film development
• Machine learning and Bayesian optimization accelerate parameter search
• System integrates automated data acquisition with spectral analysis