Examples#
The tutorials below cover the full pyscal API — from loading structures to computing structural descriptors.
Getting started
Loading structures with ASE, the pyscal workflow, and where results are stored.
Creating structures
Built-in crystal types, elements by name, custom lattices, and grain boundaries.
Finding neighbors
Cutoff (fixed / adaptive / SANN), Voronoi, and number-based neighbor methods.
Steinhardt parameters
Bond-orientational order parameters q_l and their neighbor-averaged variants.
Common neighbor analysis
Adaptive CNA and conventional CNA for identifying FCC, HCP, BCC, and icosahedral environments.
Voronoi tessellation
Voronoi structure vector (n3, n4, n5, n6) and Voronoi-based neighbor finding.
Disorder parameter
Quantifying structural disorder using Steinhardt parameter correlations.
Angular & chi parameters
Angular criteria for diamond detection and Ackland-Jones chi parameters.
Centrosymmetry parameter
Detecting defects and broken symmetry in ordered crystals.
Entropy parameter
Pair-entropy for distinguishing crystal structures, with local and averaged variants.
Short-range order
Warren-Cowley chemical short-range order parameter for multi-component alloys.
Solid/liquid clustering
Identifying solid atoms in a melt and clustering by arbitrary conditions.
Trajectory module
Efficient analysis of multi-frame LAMMPS dump trajectories.
Wigner W parameters
Third-order bond-orientational invariants w_l and their averaged variants.
Minkowski structure metrics
Voronoi face-area weighted Steinhardt parameters for robust structure identification.
Ackland-Jones classification
Angular-distribution-based structure classification for FCC, BCC, HCP, and icosahedral.
Coordination variants
Coordination number computed via cutoff, Voronoi, and SANN neighbor methods.
Angular bond distributions
Bond-angle distribution functions for characterizing local environments.
Deformation descriptors
Per-atom deformation gradient, strain, and slip-vector descriptors.
Wigner-Seitz defects
Vacancy and interstitial detection via Wigner-Seitz cell analysis.
ACE descriptors
Atomic Cluster Expansion descriptors for machine-learning interatomic potentials.