Perovskite Formability Screening

Calculate Revised Tolerance Factor

engine tolerance_factor_calculator

config (1 key)

{
  "variables": [
    "revised_tolerance_factor",
    "goldschmidt_tolerance_factor",
    "octahedral_factor"
  ]
}

expected results (1 key)

{
  "revised_tolerance_factor": {
    "note": "tau \u003c 4.18 predicts perovskite formability",
    "range": [
      0,
      5
    ]
  }
}

Correlation Matrix of Geometric Descriptors

engine correlation_matrix

config (1 key)

{
  "variables": [
    "revised_tolerance_factor",
    "goldschmidt_tolerance_factor",
    "octahedral_factor",
    "perovskite_decomposition_energy"
  ]
}

expected results (2 keys)

{
  "goldschmidt_tolerance_factor↔revised_tolerance_factor": {
    "direction": "negative",
    "note": "tau and t are inversely related by construction"
  },
  "revised_tolerance_factor↔perovskite_decomposition_energy": {
    "direction": "negative"
  }
}

Logistic Regression: tau + mu → Formability

engine logistic_regression←calc_tolerance_factor

config (3 keys)

{
  "classification": true,
  "predictors": [
    "revised_tolerance_factor",
    "octahedral_factor"
  ],
  "target": "perovskite_formability"
}

expected results (2 keys)

{
  "accuracy": {
    "min": 0.85,
    "note": "Bartel reports 92% accuracy with tau alone"
  },
  "revised_tolerance_factor_coefficient": {
    "direction": "negative",
    "note": "Higher tau = less likely perovskite"
  }
}

Random Forest: All Descriptors → Formability

engine random_forest←calc_tolerance_factor

config (3 keys)

{
  "classification": true,
  "predictors": [
    "revised_tolerance_factor",
    "goldschmidt_tolerance_factor",
    "octahedral_factor",
    "halide_perovskite_bandgap",
    "perovskite_decomposition_energy"
  ],
  "target": "perovskite_formability"
}

expected results (2 keys)

{
  "accuracy": {
    "min": 0.9
  },
  "feature_importance_rank_1": {
    "expected": "revised_tolerance_factor"
  }
}