GLOMOS

GLobal Optimization of MOlecular Systems — a genetic algorithm for the global optimization of atomic clusters and molecules, built on top of AEGON.

GLOMOS orchestrates the genetic algorithm (initial population, crossover, mutation, fitness-proportional selection, structural deduplication, stop criteria) and delegates every energy evaluation and local optimization to AEGON's calculator registry. GLOMOS itself never talks to a quantum chemistry code or a potential directly — it only calls aegon.calculators.build(calc_type, **kwargs), so any calculator AEGON supports is automatically available to the genetic algorithm.

Table of Contents

• Features
• Installation
• Dependencies
• The INPUT file
• Supported calculators
• Stochastic kick search
• Genetic algorithm search
• Rotamer / conformer search
• Citation
• Authors
• License

Features

• Three search drivers — heuristic_kick.py (staged local-opt + discrimination, no evolutionary operators), heuristic_ga.py (roulette-wheel GA over clusters/crystals), heuristic_ga_rotamers.py (GA over dihedral angles for conformer search).
• Calculator-agnostic — every driver dispatches through AEGON's registry: LJ, Sutton-Chen, TiO2, EMT, ANI, Gaussian, ORCA, MOPAC, GULP, VASP, selected by a single calculator key.
• Unit-consistent — one units key (eV/atomic/kcal) fixes energy and force together across every calculator.
• Plain-text input files — INPUT_GLOMOS_<CALCULATOR>_<ALGORITHM>_<SYSTEM>.txt (GEGA/KICK/RTMR), same convention as solids/examples.

Installation

pip install glomos

Requires Python >= 3.10. GLOMOS depends on AEGON with the [ani] extra (installs torch/torchani for the ANI calculator); no separate installation step is needed.

Dependencies

All other dependencies (ASE, NumPy, SciPy, Numba, Pydantic, PyYAML, joblib, torch, torchani) come transitively through aegon[ani].

The INPUT file

GLOMOS reads a plain-text input file with a composition block and key-value parameters (parsed by aegon.libstdio.read_main_input):

---COMPOSITION---
H  4
O  2
---COMPOSITION---

#EVOLUTIVE PARAMETERS:
nof_initpop             4    #Initial Population
nof_matings             2    #Number of matings
nof_mutants             2    #Number of mutants

#ENERGY UNITS: eV | atomic (Eh) | kcal (kcal/mol)
units                   kcal   #sets energy AND force units together

#DISCRIMINATION PARAMETERS:
tol_similarity          0.96
tol_energy              0.10
cutoff_energy           10.0
cutoff_population       10

#STOP CRITERION:
nof_generations         10
nof_repeats             3
nof_stagnant            5

#THEORY LEVEL:
calculator              GAUSSIAN
nof_processes           4

#OUTPUT FILE:
initial_file            initial.xyz
output_file             summary.xyz

Calculator-specific keys (metal_symbol for SC, ani_model for ANI, vasp_potcar/vasp_latsp for VASP, the ---GAUSSIAN---/---ORCA---/ ---MOPAC---/---GULP---/---VASP--- route blocks) apply only to the calculator selected. Binary paths and scratch folders come from AEGON's site configuration (python -m aegon.config show), not from this file.

Supported calculators

RTMR is limited to ANI/GAUSSIAN/ORCA/MOPAC — the rest have no organic-molecule parameterization or are otherwise not the tool for a conformer search.

Stochastic kick search

heuristic_kick.py: a population goes through number_of_stages local-opt + discrimination passes, no crossover/mutation. Template-driven engines (Gaussian, ORCA, MOPAC, GULP, VASP) take one input block per stage (---GULP1---/---GULP2---, ---INCAR1---/---KPOINTS1--- for VASP), so stages can run at different levels of theory.

from glomos.heuristic_kick import stochastic_kick

population = stochastic_kick('INPUT_GLOMOS_LJ_KICK_Mo8.txt')

Examples: x_run_glomos_{lj,sc,tio2,emt}_kick_{mo8,cu8,tio2,au8}.py, ..._gulp_kick_cu8.py, ..._{ani,orca,gaussian,vasp}_kick_h2o.py (seeded from the matching GEGA run's summary.xyz), ..._mopac_kick_si5o6.py (MNDO, self-generated population — see Genetic algorithm search), and ..._gaussian_kick_b10.py (multi-stage boron refinement).

Genetic algorithm search

heuristic_ga.py: roulette-wheel crossover/mutation, energy-cutoff + USR deduplication, stop on max generations / repeated isomers / stagnant cycles.

from glomos.heuristic_ga import genetic_algorithm

population = genetic_algorithm('INPUT_GLOMOS_GAUSSIAN_GEGA_H2O.txt')
for mol in population[:5]:
    print(mol.info['i'], mol.info['e'], mol.info['c'])

Population is written to output_file after every generation. Examples: x_run_glomos_{lj,sc,tio2,emt}_gega_{mo8,cu8,tio2,au8}.py, ..._gulp_gega_cu8.py, ..._{ani,orca,gaussian,vasp}_gega_h2o.py, ..._mopac_gega_si5o10.py (MNDO).

Running several searches in sequence

from glomos.heuristic_ga import genetic_algorithm

genetic_algorithm('INPUT_GLOMOS_LJ_GEGA_Mo8.txt')
genetic_algorithm('INPUT_GLOMOS_SC_GEGA_Cu8.txt')
genetic_algorithm('INPUT_GLOMOS_GAUSSIAN_GEGA_H2O.txt')

Rotamer / conformer search

heuristic_ga_rotamers.py: a GA over dihedral angles of a single molecule. Rotatable bonds are identified from the molecular graph of a rotamer_seed XYZ file; no COMPOSITION block.

from glomos.heuristic_ga_rotamers import conformational

population = conformational('INPUT_ROTAMERS.txt')

calculator is ANI (ani_model selects ANI1x/ANI1ccx/ANI2x), MOPAC, GAUSSIAN, or ORCA.

• examples/INPUT_GLOMOS_MOPAC_RTMR_C8H9NO2.txt / ..._ANI_RTMR_C8H9NO2.txt — paracetamol, ANI1ccx.
• examples/INPUT_GLOMOS_GAUSSIAN_RTMR_C2H6O.txt / ..._ORCA_RTMR_C2H6O.txt — ethanol, PBE0/Def2SVP.

Citation

If you use GLOMOS in your research, please cite the associated manuscript (in preparation), and the AEGON backend it builds on:

López-Castro C., Ortiz-Chi F., Merino G. An Efficient Growth Pattern Algorithm (GrowPAL) for Cluster Structure Prediction. J. Chem. Theory Comput. 2024, 20, 4939–4948.

Authors

• Aileen Garcia Cano — Facultad de Ingeniería, Universidad Autónoma de Yucatán, Mérida, México
• Gabriel Merino — Departamento de Física Aplicada, Cinvestav-IPN, Mérida, México
• Filiberto Ortiz-Chi — Secihti-Departamento de Física Aplicada, Cinvestav-IPN, Mérida, México

License

See LICENSE.