QuantityCalculator module

class QuantityCalculator.QuantityCalculator

Bases: object

Derived-quantity calculator for MD post-processing.

All methods are implemented as @staticmethod functions and do not maintain internal state.

Notes

Callers are responsible for providing consistent units. This class does not validate units or sampling assumptions.

static calculateModuli(C_matrix)

Compute isotropic elastic moduli from a stiffness matrix.

Parameters:

C_matrix (array-like of shape (6, 6)) – Stiffness matrix in Voigt notation.

Returns:

(bulk_modulus, shear_modulus, youngs_modulus).

Return type:

tuple of float

static computeBulkModulus(stretch_sequence)

Fit an equation of state to compute the bulk modulus.

Collects potential energy and volume from a stretch sequence, sorts by volume, and fits a Birch–Murnaghan EOS using ASE’s ase.eos.EquationOfState.

Parameters:

stretch_sequence (sequence) – Sequence of frames containing energy and volume information.

Returns:

• float – Bulk modulus in GPa.

• Side Effects

• ————

• Writes an EOS plot image file named Ag-eos.png.

Notes

This function calls _get(frame, name), which must be provided by the surrounding codebase.

static computeDebyeTemperature(V_A3, mass_u, N, G, K)

Estimate the Debye temperature from density and elastic moduli.

Parameters:

• V_A3 (float) – Volume in Å^3.

• mass_u (float) – Total mass in atomic mass units (u).

• N (int) – Number of atoms.

• G (float) – Shear modulus (unit consistency required).

• K (float) – Bulk modulus (same unit convention as G).

Returns:

Debye temperature in Kelvin.

Return type:

float

Notes

The factor / 10.18 is used as a unit conversion (comment suggests conversion related to fs/Å). Ensure G, K, and density are consistent with this convention.

static computeLindemannIndex(msd, nearest_neighbour)

Compute the Lindemann index.

Parameters:

• msd (float) – Mean-squared displacement (typically in Å^2).

• nearest_neighbour (float) – Nearest-neighbour distance (typically in Å).

Returns:

Lindemann index (dimensionless).

Return type:

float

static computeSelfDiffusionCoefficient(msd_list, sample_spacing)

Estimate self-diffusion coefficient from mean-squared displacement (MSD).

Uses the Einstein relation with an endpoint slope estimate:

D ≈ (MSD_end - MSD_0) / (6 * (t_end - t_0))

Parameters:

• msd_list (array-like) – Mean-squared displacement values (typically in Å^2).

• sample_spacing (float) – Time between MSD samples (typically in fs).

Returns:

Diffusion coefficient in Å^2/fs (if MSD is Å^2 and time is fs).

Return type:

float

Notes

This uses only the first and last MSD points. For better statistics, a linear fit over a diffusive regime is often preferred.

static computeSpecificHeatNVT(E_tot_seq, total_mass_amu, T)

Compute specific heat capacity from total energy fluctuations (NVT).

Parameters:

• E_tot_seq (array-like) – Total energy time series (typically in eV).

• total_mass_amu (float) – Total mass of the system in atomic mass units (amu).

• T (float) – Temperature in Kelvin.

Returns:

Specific heat capacity in eV/(amu·K), assuming energies are in eV.

Return type:

float

Notes

This uses a fluctuation expression based on the variance of the total energy time series.

static nearestNeighborsMean(atoms_sequence, start, end=None)

Compute mean nearest-neighbour distance over a range of configurations.

For each configuration in the interval [start, end), builds an ASE neighbour list using natural_cutoffs and computes each atom’s nearest-neighbour distance. The return value is the mean over all atoms and all configurations in the interval.

Parameters:

• atoms_sequence (sequence of ase.Atoms) – Sequence of atomic configurations.

• start (int) – Start index (inclusive).

• end (int, optional) – End index (exclusive). If None, uses start + 1.

Returns:

Mean nearest-neighbour distance in Å, or None if no neighbours could be found for some atom.

Return type:

float or None