Tag: Quantum ESPRESSO
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Machine Learning Achieves 95% Accuracy In Optimized K-Point Mesh Generation For Quantum ESPRESSO
Overview: Bridging ML and Quantum ESPRESSO for efficient DFT Density functional theory (DFT) is a cornerstone of modern materials science, enabling researchers to predict properties and behaviors of complex systems. Yet, the accuracy and efficiency of DFT calculations depend heavily on the choice of computational settings, especially the k-point mesh used to sample the Brillouin…
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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…