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Workflow and First Steps

SALTED workflow

  1. Train SAGPR model (Part 1: generate training data and Part 2: learn the density)
    1. Calculate electron density and density fitting (DF) coefficients by FHI-aims.
    2. Generate (optionally sparse) \(\lambda\)-SOAP descriptors by rascaline, and sparsify the atomic environments by farthest point sampling (FPS) method.
    3. Calculate RKHS related quantities, including kernel matrix \(\mathbf{K}_{MM}\), associated projectors, and the feature vector \(\mathbf{\Psi}_{ND}\).
    4. Optimize GPR weights by either direct inversion or CG method, and save the optimized weights.
    5. Validate the model if necessary.
  2. Predict the density and calculate derived properties of new structures (Part 3: predict properties)
    1. Predict density fitting coefficients using the GPR weights obtained in the previous step. Save the predicted density coefficients.
    2. Read the predicted density coefficients and density fitting coefficients with FHI-aims and run one diagonalization of the KS Hamiltonian (\(\rho \rightarrow H_{KS} \rightarrow \text{everything}\))
    3. Parse the output files of AIMS for derived properties, e.g. total/XC/electrostatic energy, forces, etc. Note that some quantities which depend only on the density (e.g. dipoles, electrostatic energy, etc.) do not require the diagonalization above.