Usage ===== The main command-line entry point is ``pyar-cli``. Examples: .. code-block:: bash pyar-cli -r A.xyz B.xyz -N 8 -gmin 100 -gmax 1000 --software xtb pyar-cli -a C H -as 1 4 -N 8 pyar-cli --aggregate --formula C5H4 -N 8 The repository README contains additional examples for clustering, aggregation, solvation, and reaction searches. Energy tables ------------- To print a relative-energy table from raw ``.xyz`` files, use: .. code-block:: bash pyar-energy-table *.xyz The command prints absolute energies, relative energies in kcal/mol, and the global minimum directly to the terminal. Reaction trace analysis ----------------------- To summarize a recorded AFIR trace and optionally generate PNG plots: .. code-block:: bash pyar-reaction-trace . pyar-reaction-trace . --plot pyar-reaction-trace . --plot-only pyar-cli trace . --plot The command writes ``path_summary.csv`` and ``candidate_ts/`` in the job directory unless ``--plot-only`` is used, and places plots in ``trace_plots/`` unless ``--plot-directory`` is set. The summary distinguishes the physical backend energy from the AFIR-biased optimization objective: * ``backend_energy_hartree``: backend electronic, ML, or xTB energy without AFIR * ``afir_energy_hartree``: artificial AFIR contribution * ``total_energy_hartree``: optimization objective used by geomeTRIC * ``backend_relative_kcalmol``: backend-energy change relative to the first recorded trace frame The candidate file ``candidate_ts/highest_backend_energy.xyz`` is usually the first structure to inspect for future NEB, string, dimer, or TS workflows. It is not a confirmed transition state. Utilities --------- Several smaller helper commands are available for inspection and benchmarking: .. code-block:: bash pyar-energy-table *.xyz pyar-clustering *.xyz -a maxmin -n 8 pyar-similarity -f "*.xyz" -t 0.005 pyar-descriptor *.xyz pyar-trial-generation -N 8 -i seed.xyz monomer.xyz --plot pyar-optimiser structure.xyz -c 0 -m 1 --software xtb ``pyar-energy-table`` prints relative energies, ``pyar-clustering`` selects a diverse subset, ``pyar-similarity`` reports near-duplicate structures, ``pyar-descriptor`` writes compact cluster descriptors, ``pyar-trial-generation`` builds candidate orientations, and ``pyar-optimiser`` runs the standalone geometry optimizer.