### Install MindlessGen from Source (Bash)

Source: https://github.com/grimme-lab/mindlessgen/blob/main/README.md

Clones the `mindlessgen` repository from GitHub and installs it from the source code using pip. This method is useful for installing the latest development version or when contributing to the project.

```bash
git clone https://github.com/grimme-lab/MindlessGen.git
pip install .
```

--------------------------------

### Install MindlessGen from PyPI (Bash)

Source: https://github.com/grimme-lab/mindlessgen/blob/main/README.md

Installs the latest release version of the `mindlessgen` package from the Python Package Index (PyPI) using pip. This command assumes you are in an activated virtual environment.

```bash
pip install mindlessgen
```

--------------------------------

### Install Pre-commit Hooks (Bash)

Source: https://github.com/grimme-lab/mindlessgen/blob/main/README.md

Activates the `pre-commit` hooks for the `mindlessgen` project. These hooks run checks before each commit, helping to maintain code quality and consistency.

```bash
pre-commit install
```

--------------------------------

### Install MindlessGen with Development Dependencies (Bash)

Source: https://github.com/grimme-lab/mindlessgen/blob/main/README.md

Installs `mindlessgen` in editable mode (`-e`) along with development tools like `ruff`, `mypy`, `tox`, `pytest`, and `pre-commit`. This is recommended for developers working on the `mindlessgen` codebase.

```bash
git clone https://github.com/grimme-lab/MindlessGen.git
pip install -e '.[dev]'
```

--------------------------------

### Distance Constraints for xTB Optimization in Python

Source: https://context7.com/grimme-lab/mindlessgen/llms.txt

Configures and applies distance constraints during xTB geometry optimization for a water-like molecule. It shows how to define constraints using element symbols or atomic numbers and provides examples of creating constraints from strings, atomic numbers, and TOML-like dictionaries. Requires `mindlessgen.prog` and `mindlessgen.generator`.

```python
from mindlessgen.prog import ConfigManager, DistanceConstraint
from mindlessgen.generator import generator

config = ConfigManager()

# Configure for water-like molecule with O-H constraints
config.generate.element_composition = "H:2-2,O:1-1"
config.generate.fixed_composition = True
config.generate.min_num_atoms = 3
config.generate.max_num_atoms = 3

# Define distance constraints (element symbols or atomic numbers)
constraint1 = DistanceConstraint.from_cli_string("H,O,1.02")  # 1.02 Å O-H distance
constraint2 = DistanceConstraint(atom_a=1, atom_b=8, distance=1.02)  # Same using atomic numbers

config.xtb.distance_constraints = [constraint1]
config.xtb.distance_constraint_force_constant = 0.5  # Force constant for all constraints

# From TOML-like dictionary
constraint_dict = {
    "pair": ["O", "H"],
    "distance": 1.02
}
constraint3 = DistanceConstraint.from_mapping(constraint_dict)

# Configure refinement
config.refine.engine = "xtb"
config.refine.ncores = 2
config.xtb.level = 2

# Validate constraints against composition
config.check_config(verbosity=1)

try:
    molecules, exitcode = generator(config)
    for mol in molecules:
        print(f"Generated {mol.name}: {mol.sum_formula()}")
        print(f"Coordinates:\n{mol.xyz}")
except ValueError as e:
    print(f"Configuration error: {e}")
```

--------------------------------

### Molecule Class Operations in Python

Source: https://context7.com/grimme-lab/mindlessgen/llms.txt

Demonstrates how to create, manipulate, and save/load `Molecule` objects using the `mindlessgen.molecules` module. It covers initializing a molecule, setting atomic properties and coordinates, getting the molecular formula, writing to an XYZ file, reading from an XYZ file, reading from a TURBOMOLE coord file, accessing properties, and copying molecules. Requires `numpy`.

```python
from mindlessgen.molecules import Molecule
from pathlib import Path
import numpy as np

# Create molecule from scratch
mol = Molecule(name="test_molecule")
mol.num_atoms = 3
mol.charge = 0
mol.uhf = 0
mol.atlist = np.array([8, 1, 1])  # O, H, H (0-indexed)
mol.ati = np.array([8, 1, 1])
mol.xyz = np.array([
    [0.0, 0.0, 0.0],      # Oxygen
    [0.96, 0.0, 0.0],     # Hydrogen 1
    [-0.24, 0.93, 0.0]    # Hydrogen 2
])

# Get molecular formula
formula = mol.sum_formula()  # Returns "H2O1"
print(f"Formula: {formula}")

# Write to XYZ file
mol.write_xyz_to_file("water.xyz")

# Read molecule from file
loaded_mol = Molecule.read_mol_from_file("water.xyz")
print(f"Loaded: {loaded_mol.name}")
print(f"Atoms: {loaded_mol.num_atoms}")
print(f"Charge: {loaded_mol.charge}")
print(f"Coordinates:\n{loaded_mol.xyz}")

# Read from TURBOMOLE coord format
# mol_from_coord = Molecule.read_mol_from_coord("coord")

# Access molecular properties
print(f"Atomic numbers: {mol.ati}")
print(f"Element counts: {mol.atlist}")
print(f"Unpaired electrons: {mol.uhf}")

# Copy molecule
mol_copy = Molecule()
mol_copy.xyz = mol.xyz.copy()
mol_copy.ati = mol.ati.copy()
mol_copy.num_atoms = mol.num_atoms
mol_copy.charge = mol.charge
```

--------------------------------

### Run Optional Tests with Pytest (Bash)

Source: https://github.com/grimme-lab/mindlessgen/blob/main/README.md

Executes the tests for `mindlessgen`, including those that depend on external libraries like `xtb`. This command is useful for verifying the installation and functionality, especially after making code changes.

```bash
pytest -vv --optional
```

--------------------------------

### Create and Activate Conda Environment for MindlessGen (Bash)

Source: https://github.com/grimme-lab/mindlessgen/blob/main/README.md

This snippet demonstrates how to create a new Conda environment named 'mindlessgen' with Python 3.12 and then activate it. This is a prerequisite for installing the `mindlessgen` package.

```bash
mamba create -n mindlessgen python=3.12
mamba activate mindlessgen
```

--------------------------------

### Display MindlessGen Command Line Help (Bash)

Source: https://github.com/grimme-lab/mindlessgen/blob/main/README.md

Displays all available command-line options and usage instructions for the `mindlessgen` program. This is a quick way to understand the program's capabilities and how to interact with it.

```bash
mindlessgen -h
```

--------------------------------

### Custom QM Engine Configuration for Refinement and Postprocessing

Source: https://context7.com/grimme-lab/mindlessgen/llms.txt

This Python snippet shows how to configure MindlessGen to use external quantum mechanical engines for molecule refinement and postprocessing. It demonstrates setting up xTB for fast refinement and ORCA for more detailed postprocessing, including optimization. The configuration includes specifying paths to executables, calculation parameters like functional and basis sets, and handling temporary directories.

```python
from mindlessgen.prog import ConfigManager
from mindlessgen.generator import generator

config = ConfigManager()

# Molecule generation
config.general.num_molecules = 5
config.generate.min_num_atoms = 10
config.generate.max_num_atoms = 15
config.generate.element_composition = "C:3-5,H:6-12,N:1-2"

# xTB for refinement
config.refine.engine = "xtb"
config.refine.ncores = 2
config.xtb.xtb_path = "/usr/local/bin/xtb"
config.xtb.level = 2  # GFN2-xTB

# ORCA for postprocessing
config.general.postprocess = True
config.postprocess.engine = "orca"
config.postprocess.optimize = True
config.postprocess.opt_cycles = 50
config.postprocess.ncores = 8

config.orca.orca_path = "/opt/orca_5_0_4/orca"
config.orca.functional = "r2SCAN-3c"  # Modern meta-GGA composite method
config.orca.basis = "def2-mTZVPP"     # For heavy elements
config.orca.gridsize = 2
config.orca.scf_cycles = 150

# Alternative: TURBOMOLE
# config.postprocess.engine = "turbomole"
# config.turbomole.ridft_path = "/path/to/ridft"
# config.turbomole.jobex_path = "/path/to/jobex"
# config.turbomole.functional = "pbe"
# config.turbomole.basis = "def2-TZVP"

# Set temporary directory for QM calculations
config.general.tmp_dir = "/scratch/mindlessgen_temp"

try:
    molecules, exitcode = generator(config)
    print(f"Generated {len(molecules)} optimized molecules")

    for mol in molecules:
        print(f"{mol.name}: {mol.sum_formula()} "
              f"({mol.num_atoms} atoms, charge={mol.charge})")

except RuntimeError as e:
    print(f"QM engine error: {e}")
```

--------------------------------

### Execute MindlessGen via Command Line Interface

Source: https://context7.com/grimme-lab/mindlessgen/llms.txt

The command-line interface allows users to trigger molecular generation with specific composition, constraints, and post-processing engines. It supports runtime flags for parallel execution, verbosity levels, and engine-specific parameters.

```bash
mindlessgen --config /path/to/config.toml
mindlessgen --num-molecules 10 --parallel 8
mindlessgen --element-composition "C:2-5,H:4-10,O:1-2" --min-num-atoms 10 --max-num-atoms 20
mindlessgen --postprocess --postprocess-engine orca --orca-functional PBE --orca-basis def2-SVP
```

--------------------------------

### Manage Configuration via TOML and ConfigManager

Source: https://context7.com/grimme-lab/mindlessgen/llms.txt

The ConfigManager class allows for loading settings from TOML files and overriding them programmatically. It supports flexible element composition definitions using either atomic numbers or element symbols.

```python
from mindlessgen.prog import ConfigManager

config = ConfigManager(config_file="custom_config.toml")
config.general.num_molecules = 20
config.generate.element_composition = {
    5: (1, 2),    # B: 1-2 atoms
    6: (2, 4)     # C: 2-4 atoms
}
config.generate.forbidden_elements = [57, 58, 59, 60]
```

--------------------------------

### Print MindlessGen Configuration (Bash)

Source: https://github.com/grimme-lab/mindlessgen/blob/main/README.md

Prints the active configuration of `mindlessgen`, including any default values. This command helps in understanding the current settings and debugging configuration issues.

```bash
mindlessgen --print-config
```

--------------------------------

### Create Generation Configuration and Generate Molecule

Source: https://context7.com/grimme-lab/mindlessgen/llms.txt

This Python snippet demonstrates how to create a generation configuration object, set various parameters like atom count ranges, element composition, and forbidden elements, and then use it to generate a random molecule. It includes error handling for the generation process and prints details of the generated molecule.

```python
gen_config = GenerateConfig()
gen_config.min_num_atoms = 8
gen_config.max_num_atoms = 12
gen_config.init_coord_scaling = 3.0
gen_config.increase_scaling_factor = 1.1
gen_config.scale_fragment_detection = 1.25
gen_config.scale_minimal_distance = 0.8
gen_config.contract_coords = True

# Element composition dictionary (0-indexed atomic numbers)
gen_config.element_composition = {
    0: (2, 10),   # H: 2-10 atoms
    5: (1, 2),    # C: 1-2 atoms
    6: (2, 4),    # N: 2-4 atoms
    7: (1, 3),    # O: 1-3 atoms
}

# Forbidden elements
gen_config.forbidden_elements = list(range(57, 72))  # Lanthanides

# Fixed charge
gen_config.molecular_charge = 0

# Generate random molecule
verbosity = 2
try:
    mol = generate_random_molecule(gen_config, verbosity)

    print(f"\nGenerated molecule:")
    print(f"Name: {mol.name}")
    print(f"Formula: {mol.sum_formula()}")
    print(f"Number of atoms: {mol.num_atoms}")
    print(f"Charge: {mol.charge}")
    print(f"Unpaired electrons: {mol.uhf}")
    print(f"Atom types: {mol.ati}")
    print(f"Coordinates:\n{mol.xyz}")

    # Write output
    mol.write_xyz_to_file()

except RuntimeError as e:
    print(f"Generation failed: {e}")
```

--------------------------------

### Validate and Display Configuration, Generate and Save Molecules

Source: https://context7.com/grimme-lab/mindlessgen/llms.txt

Validates the current configuration, displays it, generates molecules based on the configuration, and saves the generated molecules to XYZ files along with their sum formulas. This snippet demonstrates the basic workflow of the generator.

```python
from pathlib import Path

# Assuming 'config' and 'generator' are already imported and configured
# config.check_config(verbosity=1)

# Display active configuration
# print(config)

# Generate molecules
# molecules, exitcode = generator(config)

# Save molecules with metadata
# for mol in molecules:
#     output_path = Path(f"mlm_{mol.name}.xyz")
#     mol.write_xyz_to_file(str(output_path))
#     print(f"Wrote {output_path}: {mol.sum_formula()}")
```

--------------------------------

### Define Element Composition for Molecule Generation (Python)

Source: https://github.com/grimme-lab/mindlessgen/blob/main/README.md

This Python script demonstrates how to configure MindlessGen for generating molecules with specific elemental compositions. It shows how to set the number of atoms for each element, define forbidden elements, and configure general and generation-specific settings. The script also includes error handling for the generation process and prints properties of the generated molecules.

```python
import warnings

from mindlessgen.generator import generator
from mindlessgen.prog import ConfigManager


def main():
    """
    Main function for execution of MindlessGen via Python API.
    """
    config = ConfigManager()

    # General settings
    config.general.max_cycles = 500
    config.general.parallel = 6
    config.general.verbosity = -1
    config.general.num_molecules = 2
    config.general.postprocess = False
    config.general.write_xyz = False

    # Settings for the random molecule generation
    config.generate.min_num_atoms = 10
    config.generate.max_num_atoms = 15
    config.generate.element_composition = "Ce:1-1"
    # alternatively as a dictionary: config.generate.element_composition = {39:(1,1)}
    # or: config.generate.element_composition = {"Ce":(1,1)"}
    # or as mixed-key dict, e.g. for Ce and O: {"Ce":(1,1), 7:(2,2)}
    config.generate.forbidden_elements = "21-30,39-48,57-80"
    # alternatively as a list: config.generate.forbidden_elements = [20,21,22,23] # 24,25,26...

    # xtb-related settings
    config.xtb.level = 1

    try:
        molecules, exitcode = generator(config)
    except RuntimeError as e:
        print(f"\nGeneration failed: {e}")
        raise RuntimeError("Generation failed.") from e
    if exitcode != 0:
        warnings.warn("Generation completed with errors for parts of the generation.")
    for molecule in molecules:
        molecule.write_xyz_to_file()
        print(
            "\n###############\nProperties of molecule "
            + f"'{molecule.name}' with formula {molecule.sum_formula()}:"
        )
        print(molecule)


if __name__ == "__main__":
    main()

```

--------------------------------

### Parallel Batch Molecule Generation

Source: https://context7.com/grimme-lab/mindlessgen/llms.txt

This Python snippet configures and executes high-throughput molecule generation in parallel. It utilizes a `ConfigManager` to set parameters for parallel execution, molecule diversity, and fast QM refinement (xTB). The script generates a specified number of molecules, handles potential errors, and analyzes the results, including unique molecular formulas and writing a summary file.

```python
from mindlessgen.prog import ConfigManager
from mindlessgen.generator import generator
import warnings

config = ConfigManager()

# Parallel execution settings
config.general.parallel = 16  # Use 16 CPU cores
config.general.num_molecules = 100  # Generate 100 molecules
config.general.max_cycles = 300  # Max attempts per molecule
config.general.verbosity = 0  # Silent mode for parallel runs
config.general.write_xyz = True

# Molecule parameters for diversity
config.generate.min_num_atoms = 5
config.generate.max_num_atoms = 20
config.generate.element_composition = "C:1-5,H:1-*,N:0-2,O:0-2,S:0-1"
config.generate.forbidden_elements = "57-71,89-103"  # No f-block

# Fast refinement
config.refine.engine = "xtb"
config.refine.ncores = 2
config.refine.max_frag_cycles = 5
config.xtb.level = 1  # GFN1-xTB for speed

# Optional postprocessing (disabled for throughput)
config.general.postprocess = False

# Validate configuration
config.check_config(verbosity=1)

# Generate molecules in parallel
print(f"Starting generation of {config.general.num_molecules} molecules...")
try:
    molecules, exitcode = generator(config)

    print(f"\nGeneration complete!")
    print(f"Successfully generated: {len(molecules)}/{config.general.num_molecules}")

    if exitcode != 0:
        warnings.warn(f"Some molecules failed to generate (exitcode: {exitcode})")

    # Analyze results
    formulas = [mol.sum_formula() for mol in molecules]
    unique_formulas = set(formulas)
    print(f"Unique molecular formulas: {len(unique_formulas)}")

    # Export summary
    with open("generation_summary.txt", "w") as f:
        for mol in molecules:
            f.write(f"{mol.name}\t{mol.sum_formula()}\t{mol.num_atoms}\t{mol.charge}\n")

except RuntimeError as e:
    print(f"Critical error: {e}")
```

--------------------------------

### Programmatic Molecule Generation with Python API

Source: https://context7.com/grimme-lab/mindlessgen/llms.txt

Use the Python API to configure complex generation workflows. This involves initializing a ConfigManager object to define generation parameters, refinement settings, and post-processing instructions before calling the generator function.

```python
from mindlessgen.generator import generator
from mindlessgen.prog import ConfigManager

config = ConfigManager()
config.general.max_cycles = 500
config.generate.element_composition = "C:2-4,H:4-*,O:1-2,N:0-1"
config.refine.engine = "xtb"

molecules, exitcode = generator(config)
if exitcode == 0:
    for mol in molecules:
        print(f"Molecule: {mol.name}, Formula: {mol.sum_formula()}")
```

--------------------------------

### BibTeX Citation for original mindless DFT benchmarking

Source: https://github.com/grimme-lab/mindlessgen/blob/main/README.md

Provides the BibTeX entry for citing the original 2009 J. Chem. Theory Comput. paper that introduced the concept of mindless molecules for benchmarking.

```BibTeX
@article{korth_mindless_2009,
	title = {Mindless {DFT} benchmarking},
	volume = {5},
	issn = {15499618},
	url = {https://pubs.acs.org/doi/full/10.1021/ct800511q},
	doi = {10.1021/ct800511q},
	number = {4},
	urldate = {2022-11-07},
	journal = {J. Chem. Theo. Comp.},
	author = {Korth, Martin and Grimme, Stefan},
	month = apr,
	year = {2009},
	note = {Publisher: American Chemical Society},
	pages = {993--1003}
}
```

--------------------------------

### BibTeX Citation for mindlessgen implementation

Source: https://github.com/grimme-lab/mindlessgen/blob/main/README.md

Provides the BibTeX entry for citing the 2025 J. Chem. Inf. Model. paper describing the mindlessgen Python implementation and associated benchmarks.

```BibTeX
@article{gasevicChemicalSpaceExploration2025,
  title = {Chemical Space Exploration with Artificial "Mindless" Molecules},
  author = {Gasevic, Thomas and M{"u}ller, Marcel and Sch{"o}ps, Jonathan and Lanius, Stephanie and Hermann, Jan and Grimme, Stefan and Hansen, Andreas},
  year = 2025,
  month = sep,
  journal = {Journal of Chemical Information and Modeling},
  volume = {65},
  number = {18},
  pages = {9576--9587},
  publisher = {American Chemical Society},
  issn = {1549-9596},
  doi = {10.1021/acs.jcim.5c01364},
  urldate = {2025-10-14}
}
```

--------------------------------

### Symmetrization Operations in Python

Source: https://context7.com/grimme-lab/mindlessgen/llms.txt

Enables the generation of symmetric molecular complexes using various symmetry operations like mirror, C2 rotation, C3 rotation, and inversion. This snippet configures the generator for symmetrization, sets symmetry parameters, and optionally includes postprocessing for geometry relaxation using ORCA and xTB. Requires `mindlessgen.prog` and `mindlessgen.generator`.

```python
from mindlessgen.prog import ConfigManager
from mindlessgen.generator import generator

config = ConfigManager()

# Enable symmetrization
config.general.symmetrization = True
config.general.postprocess = True  # Recommended for relaxation

# Generate base fragment
config.generate.min_num_atoms = 5
config.generate.max_num_atoms = 10
config.generate.element_composition = "C:2-3,H:3-6,N:0-1"

# Configure symmetry operation
config.symmetrization.distance = 3.0  # Distance between fragments (Å)

# Mirror symmetry (reflection across plane)
config.symmetrization.operation = "mirror"

# C2 rotation (180° rotation)
# config.symmetrization.operation = "c_2_rotation"

# C3 rotation (120° rotation)
# config.symmetrization.operation = "c_3_rotation"

# Inversion symmetry
# config.symmetrization.operation = "inversion"

# Postprocessing for geometry relaxation
config.postprocess.engine = "orca"
config.postprocess.optimize = True
config.refine.engine = "xtb"

try:
    molecules, exitcode = generator(config)
    for mol in molecules:
        print(f"Symmetric molecule: {mol.name}")
        print(f"Formula: {mol.sum_formula()}")
        print(f"Total atoms: {mol.num_atoms}")
except RuntimeError as e:
    print(f"Symmetrization failed: {e}")
```

--------------------------------

### Random Molecule Generation Function in Python

Source: https://context7.com/grimme-lab/mindlessgen/llms.txt

Provides a low-level function for creating random molecular structures with customizable parameters. This is useful for generating diverse molecular datasets or exploring chemical space. Requires `mindlessgen.molecules` and `mindlessgen.prog`.

```python
from mindlessgen.molecules import generate_random_molecule
from mindlessgen.prog import GenerateConfig

# Example usage (assuming GenerateConfig and generate_random_molecule are imported)
# random_mol = generate_random_molecule(GenerateConfig())
# print(random_mol)
```

--------------------------------

### BibTeX Citation for CEH model

Source: https://github.com/grimme-lab/mindlessgen/blob/main/README.md

Provides the BibTeX entry for the publication describing the Charge Extended Hückel (CEH) model, representing the first use of the current implementation.

```BibTeX
@article{
    doi:10.1021/acs.jpca.4c06989,
    author = {M{"u}ller, Marcel and Froitzheim, Thomas and Hansen, Andreas and Grimme, Stefan},
    title = {Advanced Charge Extended Hückel (CEH) Model and a Consistent Adaptive Minimal Basis Set for the Elements Z = 1–103},
    journal = {The Journal of Physical Chemistry A},
    volume = {128},
    number = {49},
    pages = {10723-10736},
    year = {2024},
    doi = {10.1021/acs.jpca.4c06989},
    note ={PMID: 39621818},
    URL = {https://doi.org/10.1021/acs.jpca.4c06989},
    eprint = {https://doi.org/10.1021/acs.jpca.4c06989}
}
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.