New Paper Published in Computational Materials Today

The study presents a machine-learning-based approach for modeling double perovskite materials, enabling the simultaneous prediction of their bandgap and dielectric properties. By employing multi-output machine learning, the work demonstrates an efficient strategy for screening and understanding materials with promising electronic and dielectric characteristics, supporting the accelerated discovery and design of novel functional materials.

Congratulations to A. Kumari and A. Shrivastavaon this publication and their excellent work!

Read the full story here:
Kumari, A., Shrivastava, A., & Adam, J. (2026). Machine learning-based modeling of double perovskites via multi-output prediction of bandgap and dielectric properties. Computational Materials Today, 11, 100059. https://doi.org/10.1016/j.commt.2026.100059