atomchain 0.1.7

A library to chain the atomic models with applications

atomchain

AtomChain provides CLI tools and Python APIs for atomic structure manipulation and ML potential calculations.

CLI Tools

AtomChain includes several command-line tools for common atomistic workflows:

• mlrelax - Relax atomic structures using ML potentials
• mlphonon - Calculate phonon properties and band structures
• mlgap - Predict band gap using ML potentials
• mlsinglepoint - Single point energy/forces/stress calculations
• mlsupercell - Generate supercells with various transformation matrices
• mlrattle - Generate rattled structure datasets for training
• mlbatch - Batch process trajectories with ML potentials
• mlcompare - Compare calculated properties between two trajectories
• mlneb - Nudged elastic band calculations for reaction pathways

Installation

pip install -e .

Quick Start

Single Point Calculation

mlsinglepoint input.vasp --calculator chgnet --output results.yaml

Generate Supercell

mlsupercell input.vasp --size 2 --output supercell.vasp

Generate Training Dataset

mlrattle input.vasp --stdev 0.05 --count 100 --output structures.traj

Batch Process Trajectory

mlbatch structures.traj --calculator chgnet --output results.traj

Compare Trajectories

mlcompare dft.traj ml.traj --labels "DFT" "CHGNet" --output comparison.png

Relax Structure

mlrelax input.vasp --calculator chgnet --output relaxed.vasp

Calculate Phonons

mlphonon input.vasp --calculator chgnet --supercell 2,2,2

Documentation

Detailed documentation for each tool is available in the docs/ directory:

• docs/singlepoint.md - Single point calculations
• docs/supercell.md - Supercell generation
• docs/rattle.md - Dataset generation
• docs/batch.md - Batch trajectory processing
• docs/compare.md - Trajectory comparison
• docs/relax.md - Structure relaxation
• docs/phonon.md - Phonon calculations

Python API

All CLI tools have corresponding Python APIs for programmatic use:

from ase.io import read
from atomchain import (
    relax_with_ml,
    phonon_with_ml,
    init_calc,
    calculate_single_point,
    make_supercell_structure,
    generate_rattle_dataset,
    calculate_trajectory_batch,
    compare_trajectories,
    calculate_neb,
    predict_gap,
)

atoms = read("structure.vasp")

# Relax structure
relaxed_atoms = relax_with_ml(atoms, calculator="chgnet")

# Calculate phonons
phonon_with_ml(atoms, calculator="chgnet", supercell_matrix=[[2,0,0],[0,2,0],[0,0,2]])

# Predict band gap
gap = predict_gap(atoms, xc="PBE")

# Single point calculation
results = calculate_single_point(atoms, calculator="chgnet")

# Generate supercell
supercell = make_supercell_structure(atoms, size=2)

# Generate dataset
generate_rattle_dataset(
    atoms,
    stdev=0.05,
    count=100,
    output="dataset.traj"
)

# Batch process trajectory
results = calculate_trajectory_batch(
    "dataset.traj",
    calculator="chgnet",
    output="results.traj"
)

# Compare trajectories
compare_trajectories("dft.traj", "ml.traj", labels=["DFT", "CHGNet"])

# NEB calculation
images = [read(f"image{i}.vasp") for i in range(5)]
calculate_neb(images, calculator="chgnet")

Requirements

• Python 3.9+
• ASE (Atomic Simulation Environment)
• Optional: CHGNet, M3GNet, MACE for ML potentials
• Optional: Phonopy for phonon analysis

License

[Your license here]