Tag: materials modelling
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ML Achieves 95% Accuracy in Optimized K-Point Mesh Generation for Quantum ESPRESSO
Introduction: The Challenge of K-Point Mesh Optimization in Quantum ESPRESSO Accurate materials modelling hinges on precise sampling of the electronic structure. In density functional theory (DFT) calculations, the k-point mesh determines how finely the Brillouin zone is sampled. Choosing the right mesh is crucial: too coarse a mesh yields inaccurate results, while an excessively dense…
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Machine Learning Empowers 95%-Accurate K-Point Mesh for Quantum ESPRESSO
Revolutionizing Computational Materials Science Density functional theory (DFT) is a cornerstone of modern materials research, enabling scientists to predict electronic structure and properties with quantum mechanical rigor. Yet, the reliability and efficiency of DFT calculations hinge on a critical, often tedious step: selecting an optimal k-point mesh for Brillouin-zone sampling. In large-scale studies, this choice…