CSE Faculty Publishes Research in SCI Q2 Journal

Anurag University congratulates Dr. Purnachary Munigadapa, Associate Professor in the Department of Computer Science and Engineering, on the successful publication of his research paper titled "Interpretable Machine Learning Modelling of DFT Band-Gap Underestimation from Composition-Derived Descriptors" in Materials Letters (Elsevier), a prestigious SCI Q2 indexed journal. This publication reflects his commitment to advancing research at the intersection of artificial intelligence and materials science.

The research presents an interpretable machine learning framework to address the underestimation of Density Functional Theory (DFT) band-gap predictions using composition-derived descriptors. By improving the accuracy and transparency of computational material property prediction, the study contributes to accelerating the discovery and design of advanced functional materials for scientific and industrial applications.

Interpretable artificial intelligence is becoming increasingly important in scientific research, enabling reliable and explainable predictive models across diverse domains. This publication highlights the growing role of AI in materials discovery while reinforcing the university's commitment to fostering interdisciplinary research with global scientific impact.