Interpretable AI in materials discovery: Uncovering how models make predictions
Interpretable AI in materials discovery: Uncovering how models make predictions
https://www.eurekalert.org/news-releases/1131777
Publish Date: 2026-06-14 22:13:00
Source Domain: www.eurekalert.org
image:
The proposed method combines a graph neural network with hierarchical clustering to extract key features linking crystal structure to optical spectra, and then groups materials with similar structural and spectral characteristics, revealing patterns that can guide materials design.
view more
Credit: Institute of Science Tokyo
A method to interpret artificial intelligence (AI) models used in materials discovery by analyzing their learned features has been developed by researchers from Japan. The method extracts key features from an AI model trained on atomic structural data and optical absorption spectra, and then groups materials with similar structural and spectral characteristics. This approach can be extended to reveal how atomic arrangements influence other material properties, paving the way for more efficient materials design.
In recent years, artificial intelligence (AI) has emerged as a powerful tool to predict how materials will behave based on their atomic structure, helping researchers discover new materials faster and reduce reliance on trial-and-error methods. However, many of these models work like “black boxes.” They can make accurate predictions, but they do not explain how those predictions are made. This makes it difficult to understand the relationships between a material’s structure and its properties, limiting how useful these models are for guiding the development of new designs.
Now, in a study to be published in the journal Advanced Intelligent Discovery on June 15, 2026, researchers…
