Publications by authors named "Matija Medvidovic"
Density functional theory (DFT) stands as a cornerstone method in computational quantum chemistry and materials science due to its remarkable versatility and scalability. Yet, it suffers from limitations in accuracy, particularly when dealing with strongly correlated systems. To address these shortcomings, recent work has begun to explore how machine learning can expand the capabilities of DFT: an endeavor with many open questions and technical challenges.
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- The study focuses on reducing the complexity of the four-point vertex function related to the functional renormalization group (FRG) flow in the two-dimensional t-t’ Hubbard model on a square lattice.
- Using a deep learning approach that employs a neural ordinary differential equation solver, the researchers effectively model the FRG dynamics and identify different magnetic and superconducting phases.
- The analysis reveals that only a few key modes are needed to represent the FRG dynamics, showcasing the potential of artificial intelligence to simplify and enhance our understanding of complex electron interactions in quantum field theory.