Russia Develops AI Tool to Discover New Materials for Batteries and Optics
A new platform built by chemists in Samara uses machine learning to predict electronic properties of materials, helping scientists identify promising compounds for energy storage and optical devices.
Researchers at Samara State Technical University have created an online service that accelerates the search for materials used in batteries and optics. The platform, called Band Gap Calculation, quickly estimates the band gap—the key property that determines whether a crystalline material conducts electricity.
According to Elizaveta Morkhova, a senior researcher at Samara Polytech’s International Research Center for Theoretical Materials Science, understanding a material’s band gap reveals whether it has electronic conductivity.
The online tool performs high-throughput screening of potential materials using machine learning algorithms. By analyzing more than 20 parameters, the model can identify relationships between a material’s structure and its electronic behavior with 92 percent accuracy—in just seconds. The algorithm has been optimized for compounds used in modern electronics, optics, and energy systems.
