AtomicAI

AtomicAI is a Python-based package for building machine learning models in the field of computational materials science. It can be used for machine learning force field generation, atomic structure classifications based on temperature, defects, phase, and more.

Table of Contents

1. Features
2. Installation
3. Requirements
4. Usage
5. Examples
6. Documentation
7. Contributing
8. License
9. Authors
10. Contact
11. Cite

Features

AtomicAI offers a wide range of features for computational materials science:

Analysis

• Processing and visualization of atomic coordinates in various file formats (CIF, VASP, XYZ, Conquest, etc.)
• Utilizes 'ase' library for reading and writing structure files
• Radial Distribution Function (RDF) calculation and analysis
• Structure analysis tools

Data Processing

• Classification of atoms in trajectory files
• File format conversion between different atomic structure formats

Machine Learning

• Featurization of atomic structures
• Dimension reduction techniques (PCA, LPP, TsLPP, TsNE, UMAP)
• Machine Learning Force Field (MLFF) generation
• Descriptor generation for atomic environments and forces

Clustering and Visualization

• Various clustering methods
• Data plotting and visualization tools for RDF, molecular dynamics statistics, and clustering results

Installation

Install AtomicAI using pip:

pip install AtomicAI

Requirements

AtomicAI requires Python 3.7 or higher. Other dependencies will be automatically installed during the pip installation process.

Usage

AtomicAI provides several command-line tools for various tasks. Here are some examples:

File Conversion

• cq2vasp: Convert Conquest files to VASP format
• xyz2vasp: Convert XYZ files to VASP format
• vasp2cif: Convert VASP files to CIF format
• vasp2xyz: Convert VASP files to XYZ format
• vasp2lmp_data: Convert VASP files to LAMMPS data format
• vasp2cq: Convert VASP files to Conquest format
• lmp2vasp: Convert LAMMPS trajectory to VASP format
• cif2cq: Convert CIF files to Conquest format
• cq2cif: Convert Conquest files to CIF format
• ase_traj2xyz_traj: Convert ASE trajectory to XYZ trajectory

Structure Manipulation

• supercell: Create supercell structures
• build_interface: Build interfaces between materials
• wrap2unwrap: Convert wrapped coordinates to unwrapped

Analysis

• rdf: Calculate Radial Distribution Function
• structure_analysis: Perform various structural analyses

Visualization

• plot_rdf_data: Plot RDF data
• plot_md_stats: Plot molecular dynamics statistics
• plot_vasp_md: Plot VASP molecular dynamics results
• plot_lammps_md: Plot LAMMPS molecular dynamics results
• plot_clusters: Visualize clustering results

Machine Learning

• generate_descriptors: Calculate atomic environment descriptors
• generate_force_descriptors: Generate force-related descriptors
• laaf: Calculate LAAF descriptors
• dim_reduction: Perform dimension reduction
• dim_reduction_mpi: Perform parallel dimension reduction using MPI
• pca: Perform Principal Component Analysis
• lpp: Perform Locality Preserving Projection
• optimize_tslpp_hyperparameters_without_prediction: Optimize TsLPP hyperparameters
• optimize_tslpp_hyperparameters_with_prediction: Optimize TsLPP hyperparameters with prediction
• predict_tslpp: Predict using optimized TsLPP model

LAMMPS Input Generation

• lammps_npt_inputs: Generate LAMMPS NPT input files
• lammps_nvt_inputs: Generate LAMMPS NVT input files

For more details on each tool, use the --help flag after the command.

Examples

(This section should be filled with basic examples of how to use the main features of AtomicAI. As the context doesn't provide specific examples, I'll leave this section for you to fill in with relevant use cases.)

Documentation

(Add a link to the full documentation when available. The context doesn't provide this information, so you may want to add it when documentation is ready.)

Contributing

We welcome contributions to AtomicAI! Please see our contributing guidelines for more information. (You may want to add a CONTRIBUTING.md file to your repository with detailed guidelines.)

License

AtomicAI is released under the MIT License. See the LICENSE.md file for details.

Authors

• Selva Chandrasekaran Selvaraj

Contact

• Email: selvachandrasekar.s@gmail.com
• Website: https://sites.google.com/view/selvas
• Twitter: https://twitter.com/selva_odc
• LinkedIn: https://www.linkedin.com/in/selvachandrasekaranselvaraj/
• Google Scholar: https://scholar.google.com/citations?user=vNozeNYAAAAJ&hl=en
• Scopus: https://www.scopus.com/authid/detail.uri?authorId=57225319817
• ResearchGate: https://www.researchgate.net/profile/Selva-Chandrasekaran-Selvaraj
• PyPI: https://pypi.org/project/AtomicAI/
• Documentation: https://atomicai.readthedocs.io/en/latest/
• GitHub: https://github.com/SelvaGith/AtomicAI

Cite

If you use AtomicAI in your research, please cite: