Accelerating the Calculation of Koopmans Screening Parameters Using Machine Learning

Explore an advanced Quantum ESPRESSO tutorial focusing on accelerating the calculation of Koopmans screening parameters using machine learning. Delve into the intricacies of Hubbard and Koopmans functionals from linear response, covering topics such as project overview, step-by-step processes, mapping orbital densities, local decomposition, invariance, power spectrum, and retrogression. Learn about the equipment workflow, model testing, and analysis of results, including mean model, training outcomes, baseline model comparisons, and test findings. Gain valuable insights into this cutting-edge approach for computational materials science and engage with the material through a comprehensive syllabus designed to enhance understanding of complex quantum mechanical calculations.

Intro
Project overview
Step by step
First step
Second step
Mapping orbital densities
Local decomposition
Invariance
Power spectrum
Retrogression
Equipments workflow
Testing the model
Results
Mean model
Training results
Baseline model
Test results
Conclusions
Questions