Suvo Banik
Papers
1
Total Citations
6
H-Index
1
About
Suvo Banik is a rising researcher whose work centers on the computational design and modeling of functional materials, with a particular focus on phase-change compounds. His key research areas include symbolic regression, machine learning for materials science, and the physics of strongly correlated electron systems. Banik’s most notable contribution is his pioneering use of symbolic regression to model the metal–insulator transition (IMT) temperature in alio-valently doped vanadium dioxide (VO₂), a correlated semiconductor whose near-room-temperature IMT is critical for applications in thermochromic windows, actuators, and memory devices. By developing a data-driven framework that predicts precise IMT temperatures, he has provided a powerful tool for tuning VO₂’s behavior for specific technologies, addressing a long-standing challenge in the field. His 2024 paper on this topic has already garnered 6 citations, reflecting its immediate impact. Banik’s work bridges advanced computational techniques with practical material design, offering a pathway to accelerate the discovery of next-generation smart materials. His achievements mark him as a promising young scientist at the forefront of AI-driven materials innovation.
Research Focus
Key Achievements
Top Papers
- 1