Executive Summary : | The study aims to investigate the Anomalous Nernst Effect (ANE), a phenomenon in ferromagnetic materials where an anomalous transverse voltage is produced. It uses Machine Learning models and Density Functional Theory to study the parameters responsible for ANE in Weyl semimetals (WSMs). Previous research has identified intrinsic magnetism and Weyl nodes as sources and sinks for Berry curvature in these materials. Magnetic arrangement near the Fermi level and temperature gradients also contribute to ANE in WSMs. The breaking of spatial symmetry in pairs of Weyl nodes enhances transport and ANE, suggesting a relationship between symmetry protection mechanisms and ANE. ANE is proportional to magnetization, but recent findings suggest that the Berry curvature of Bloch bands is geometrically related to ANE near the Fermi energy.
The main objective is to develop reliable models for understanding fundamental factors responsible for ANE, which are mathematically connected to the Berry curvature of Bloch bands at the Fermi energy. This will help establish key physical parameters controlling ANE in Weyl Semimetals. ML models followed by Density Functional Analysis (DFT) calculations are computationally efficient due to more screening through statistical data and accurate prediction. This approach reduces computational efforts, leads to more accurate DFT calculations, and reduces experimental trials and resource wastage. In conclusion, this study aims to find enhanced Nernst signals in Weyl materials and local magnetic behavior and magnetization in these materials, potentially producing new novel materials for thermoelectric applications. |