Synchrotron radiation data-driven artificial intelligence approaches in materials discovery

Abstract

Synchrotron radiation technology provides high-resolution and high-sensitivity information for many fields such as material science, life science, and energy research. Synchrotron radiation data-driven methods have significantly accelerated the development of materials discovery and analysis. However, synchrotron radiation data is complex and large, requiring artificial intelligence for analysis. Artificial intelligence can efficiently process complex high-dimensional data, automate the analysis process, discover hidden patterns and associations, and build predictive models. This review provides an overview of the application and development of combining synchrotron radiation data-driven methods with artificial intelligence in the field of materials discovery. The application of the method in science is still limited by the problems of large and complex synchrotron radiation data, valuable experimental machine time, and uninterpretable artificial intelligence models. To address these problems, this review correspondingly proposes solutions for synchrotron radiation artificial intelligence data banks, standardized experiment records systems, and interpretable artificial intelligence predictive models.

Keywords

• Synchrotron radiation
• Data-driven methods
• Artificial intelligence
• Materials discovery