Tools API

build_multilayers

Multilayer Structure Generator - reads VASP files, finds commensurate supercells, and stacks them.

AtomicAI.tools.build_multilayers.analyze_vectors(structures, threshold, max_multiples)[source]

Unified vector analysis function

Return type:: Tuple[List[Dict], ndarray, float]

AtomicAI.tools.build_multilayers.build_multilayer(structures, supercell_details, structure_names, mean_len, z_gap, ith_structure)[source]

Generate multilayer structure from component structures

Return type:: Atoms

AtomicAI.tools.build_multilayers.build_multilayers()[source]: Main execution function - now testing ALL permutations

AtomicAI.tools.build_multilayers.calculate_stack_position(bottom, top, z_gap)[source]

Calculate optimal z-position for stacking with specified gap

Return type:: ndarray

AtomicAI.tools.build_multilayers.create_supercell(atoms, nx, ny, nz, name, ith_structure)[source]

Generate supercell from base structure

Return type:: Atoms

AtomicAI.tools.build_multilayers.get_multiples_mapping(initial_names, permuted_names, multiples)[source]

Map multiples to permuted order

Return type:: List[int]

AtomicAI.tools.build_multilayers.get_nonredundant_permutations(files)[source]

Generate non-redundant permutations considering periodicity

Return type:: List[tuple]

AtomicAI.tools.build_multilayers.print_results(structures, results, all_lengths, threshold, structure_names)[source]

Print formatted analysis results

Return type:: None

AtomicAI.tools.build_multilayers.read_vasp_files(filenames)[source]

Read multiple VASP files with error handling

Return type:: Tuple[List[Atoms], List[str]]

AtomicAI.tools.build_multilayers.setup_logging()[source]: Initialize logging to both console and single file

surfaces

AtomicAI.tools.surfaces.structures()[source]

AtomicAI.tools.surfaces.surfaces(filename)[source]

rdf

AtomicAI.tools.rdf.RDF()[source]

AtomicAI.tools.rdf.available_pairs(atoms_list)[source]

AtomicAI.tools.rdf.moving_average(data, window_size)[source]

AtomicAI.tools.rdf.pairs(a, b, predict)[source]

AtomicAI.tools.rdf.predict_pairs(atoms_list)[source]

AtomicAI.tools.rdf.supercell(data, rMax)[source]

select_snapshots

AtomicAI.tools.select_snapshots.select_snapshots()[source]

structure_analysis

AtomicAI.tools.structure_analysis.structure_analysis()[source]

supercell

AtomicAI.tools.supercell.generate_supercell_vasp(input_file, scaling_matrix, output_vasp, cartesian=False)[source]

AtomicAI.tools.supercell.supercell()[source]