python-ace/examples/HEA/input.yaml at d0e5670e5677b48630755835e07743fe7a0eebb0 · ICAMS/python-ace · GitHub

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cutoff: 7 # cutoff for neighbour list construction
seed: 42  # random seed


#################################################################
## Metadata section
#################################################################
metadata:
  comment: Exempliar fit of HEA dataset


#################################################################
## Potential definition section
#################################################################
potential:
  deltaSplineBins: 0.001
  elements: [Cr, Fe, Ni, Mn, Co]

  # Embeddings are specified for each individual elements,
  # all parameters could be distinct for different species
  embeddings: # possible keywords: ALL, UNARY, elements: Al, Ni
    ALL: {
      npot: 'FinnisSinclairShiftedScaled',
      fs_parameters: [ 1, 1, 1, 0.5],
      ndensity: 2,
    }

  # Bonds are specified for each possible pairs of elements
  # One could use keywords: ALL (Al,Ni, AlNi, NiAl)
  bonds: # possible keywords: ALL, UNARY, BINARY, elements pairs as AlAl, AlNi, NiAl, etc...
    ALL: {
      radbase: ChebExpCos,
      radparameters: [ 5.25 ],
      rcut: 7,
      dcut: 0.01,
      NameOfCutoffFunction: cos,
    }

  ## possible keywords: ALL, UNARY, BINARY, TERNARY, QUATERNARY, QUINARY,
  ## element combinations as (Al,Al), (Al, Ni), (Al, Ni, Zn), etc...
  functions:
    ALL: {
      nradmax_by_orders: [15, 3, 3, 2, 1],
      lmax_by_orders: [ 0, 2, 2, 1, 1],
    }

    QUATERNARY: {
      nradmax_by_orders: [15, 3, 2, 1, 1],
      lmax_by_orders: [ 0, 2, 2, 1, 1]
    }

    QUINARY: {
      nradmax_by_orders: [15, 3, 2, 1, 1],
      lmax_by_orders: [ 0, 2, 2, 1, 1]
    }

#################################################################
## Dataset specification section
#################################################################
data:
  ### Option 1: pandas dataframe in pkl.gz
  filename: HEA_randII_example.pkl.gz

#################################################################
## Fit settings section
#################################################################
fit:
  loss: { kappa: 0.95, L1_coeffs: 0,  L2_coeffs: 0,  w1_coeffs: 0, w2_coeffs: 0,
          w0_rad: 0, w1_rad: 0, w2_rad: 0 }

  weighting: { type: EnergyBasedWeightingPolicy, nfit: 200, DElow: 1.0, DEup: 10.0, DE: 1000000.0, DF: 1000000.0,
               # wlow: 0.75,
               energy: convex_hull, #  "convex_hull" or "cohesive"
               seed: 42
             }

  optimizer: BFGS # L-BFGS-B # Nelder-Mead
  maxiter: 1500

  fit_cycles: 1               # (optional) number of consequentive runs of fitting algorithm, that helps convergence

  ladder_step: 5

  ladder_type: body_order

#################################################################
## Backend specification section
#################################################################
backend:
  evaluator: tensorpot  # tensorpot backend (recommended)
  batch_size: 20

  ## frequency of detailed metric calculation and printing
  display_step: 50