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A package to calculate various stoichometric features of compounds.

Project description


The module chem_calc calculates chemical and physical properties based on the stoichometry of a chemical compound.

The 20+ available properties (or features) used in this package are elemental properties from the CRC Handbook of Chemistry and Physics as well as the NIST X-ray Mass Attenuation Coefficients Database.

Available properties

CRC Handbook of Chemistry and Physics

Property / Feature Tag
Number of 4f electrons '4f'
Number of 5d electrons '5d'
Combined number of 4f and 5d electrons 'add4f5d'
Absolute value of the difference of number of 4f and 5d electrons 'sub4f5dabs'
Number of electrons 'allelectrons'
Number of valence outer shell electrons 'val_e'
Atomic number 'atomicnumber'
Atomic weight 'atomicweight'
First ionization energy 'ionenergy'
Pauling electronegativity of most common oxidation state 'el_neg_chi'
van der Waals radius 'R_vdw_element'
Covalent radius 'R_cov_element'
Atomic number to mass number ratio 'zaratio'
Excitation energy 'ex_energy'
Period 'period'
Elemental density 'density'

NIST X-ray Mass Attenuation Coefficients Database for features at 662 keV and include the following:

Property / Feature Tag
Coherent scattering coefficient 'co_scatt'
Incoherent scattering coefficient 'inco_scatt'
Photoelectric absorption 'pe_abs'
Coherent attenutation coefficient 'atten_co'
Incoherent attenutation coefficient 'atten_inco'


  • how_many -- Returns the number of atoms in a given chemical formula.
>>> import chem_calc
>>> chem_calc.chem_calc('H2O').howmany
  • total -- Returns the feature total for a given chemical formula.
>>> import chem_calc
>>> chem_calc.chem_calc('H2O',feature='atomicweight').total
  • avg_feature -- Returns the average value of a property for a given chemical formula.
>>> import chem_calc
>>> chem_calc.chem_calc('H2O',feature='atomicweight').avg_feature


Use 'pip' to install from pypi:

pip install chem_calc

or 'pip' to install from github:

pip install git+

or clone the package using git:

git clone


Requires numpy, pickle, and pandas


The original code that this project is based on In keeping with this mentality, all code is released under the MIT.

Project details

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