Revolutionizing Energy Levels: Harvard Researchers Unveil Cutting-Edge Machine Learning Technique Using Gaussian Processes
Advancements in Semilocal Density Functional Theory: A Machine Learning Approach One of the significant hurdles faced in semilocal density functional theory (DFT) is the persistent underestimation of band gaps. This challenge primarily arises from self -interaction and delocalization errors, complicating the accurate prediction of electronic properties and charge transfer processes. While hybrid DFT methods, which incorporate a portion of exact exchange energy, have shown improvements in band gap predictions, they often necessitate specific adjustments tailored to individual systems . Recently, machine learning techniques have emerged as a promising avenue for enhancing DFT accuracy, particularly concerning molecular reaction energies and systems with strong correlations. The DM21 functional exemplifies how explicitly fitting energy gaps can mitigate self-interaction errors and refine DFT predictions. Innovative Machine Learning Techniques from Harvar...