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

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
cutoff: 7.5  # cutoff for neighbour list construction
seed: 42  # random seed


#################################################################
## Metadata section
##
## This is arbitrary key (string)-value (string) pairs that would
## be added to the potential YAML file:
#################################################################
metadata:
  comment: Example of fitting simple copper potential for Cu-II dataset


#################################################################
## Potential definition section
#################################################################
potential:
  deltaSplineBins: 0.001
  elements: [ Cu ]  # list of all elements

  # 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: SBessel,
      radparameters: [ 5.25 ],
      rcut: 7.5,
      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:
    UNARY: {
      nradmax_by_orders: [15, 6, 4, 3, 2, 1],
      lmax_by_orders:    [ 0, 3, 3, 2, 2, 1],
    }


#################################################################
## Dataset specification section
#################################################################
data:
### Option 1: pandas dataframe in pkl.gz
  filename: Cu_df2_1k.pkl.gz  # force to read reference pickled dataframe from given file


  ## Test dataset specification
  test_size: 0.10  # Take 10% of train data. If test_size>1 - take test_size samples from train data

#################################################################
## Fit settings section
#################################################################
fit:
  loss: { kappa: auto, L1_coeffs: 1e-8,  L2_coeffs: 1e-8, w0_rad: 1e-8, w1_rad: 1e-8, w2_rad: 1e-8 }

## If `weighting` is not specified - uniform weighting would be used

  ## scipy.minimze algorithm: BFGS /  L-BFGS-B / Nelder-Mead / etc...
  optimizer: BFGS

  ## maximum number of scipy.minimize iterations
  maxiter: 1000

  ## EXTRA OPTIONS ##
  repulsion: auto    # set inner cutoff based on the minimal distance in the dataset

  #  see Ladder scheme fitting for more info
  ladder_step: 200

  ladder_type: power_order

#################################################################
## Backend specification section
#################################################################
backend:
  evaluator: tensorpot
  batch_size: 100
  display_step: 50