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Exploring high-performance perovskite solar cell candidates with machine learning and cluster analysis, and validating them experimentally

AI editorPublished 2026.10.06

An ACS Applied Materials & Interfaces paper reported that, after extracting microstructural features from SEM images, it combined cluster analysis with supervised and unsupervised ML to classify perovskite solar cell candidates and derived high-performance design rules through experimental validation. It is a case that combines materials-informatics exploration with experimental confirmation. [1]

1/ AI utilization method: supervised and unsupervised machine learning, including SEM image feature extraction and cluster analysis
2/ Applied task: microstructure-based candidate exploration and design rule derivation for perovskite solar cells
3/ Reported result: accelerated discovery of high-performance candidates and confirmation of design rules through experimental validation
4/ This study reported that it identified high-performance perovskite solar cell candidates by combining cluster analysis and machine learning based on microstructural information obtained from SEM images.
5/ The paper explains that it derived design rules by using experimental validation together, and accelerated the discovery of high-performance candidates.
6/ From a materials research perspective, the key is the approach of narrowing candidates by linking image-based microstructural features with performance data.
7/ Rather than simple prediction alone, it is a case of combining materials informatics and experimental materials development because it confirmed rules by connecting them with experimental results.
8/ However, this approach is heavily dependent on image quality, the feature extraction method, the choice of the number of clusters, and the representativeness of the training data.
9/ The reported results in the paper cannot be guaranteed to reproduce as is for other compositions or processing conditions, and extrapolation beyond the applicable range requires caution. [1]

How to apply this

You can use SEM images and performance data together to cluster high-performance perovskite solar cell candidates and prioritize them.

  1. Normalize SEM images with a consistent preprocessing rule and extract microstructural features.
  2. Perform clustering on the extracted features to divide the candidate pool and compare it with performance metrics.
  3. Select representative samples from each cluster and remeasure performance in independent experiments.
  4. Use the validation results to check whether the link between features and performance holds repeatedly.
Data needed
Repeatedly measured SEM images, corresponding device performance metrics, composition and process-condition metadata
Checking the result
Check whether the link between clusters and performance is maintained in samples not used for training, and validate top candidates through separate experiments.
What can go wrong
Because of image noise or limited data, clusters may reflect measurement artifacts before process variables.

이 가이드는 원문의 방법을 바탕으로 AI가 제안한 적용 방향입니다. 원문에 적힌 내용이 아니며 같은 결과가 재현된다는 보장도 아닙니다.

Related concepts and practice

이 이슈를 읽는 데 필요한 개념입니다. 용어마다 Colab에서 바로 돌려볼 수 있는 실습 노트북이 있습니다.

Sources

  1. Machine Learning-Guided Discovery of High-Performance Perovskite Solar Cells via Cluster Analysis and Experimental Validation | ACS Applied Materials & Interfaces | ACS Publications

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