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pyocse

Python Organic Crystal Simulation Environment

pyocse v0.1.3 563.1K downloads/30d#5,983 on PyPI3
Permissive license MIT AGING released

What it is and what it does

PYOCSE is a Python environment for automating organic crystal simulations focused on mechanical properties. It wraps two main workflows: first, it generates structural models and force fields by interfacing with external tools; second, it designs and executes LAMMPS-based molecular dynamics simulations to measure how organic crystals respond to mechanical loads like tension, compression, and shear. The package works with crystal structures and uses pyxtal for crystal representation.

The package is designed for materials scientists and computational chemists who need to systematically simulate mechanical behavior of organic crystals. It abstracts away much of the boilerplate for setting up LAMMPS input files, defining simulation tasks (tensile, compressive, shear), and managing supercell geometry. However, it requires external LAMMPS installation with specific modules compiled and relies on conda for environment setup.

Use it for:

  • Batch-simulate mechanical properties of organic crystals from a database to identify structure-property relationships.
  • Generate force fields and run uniaxial tensile tests on crystal structures at specified temperatures and strain rates.
  • Automate supercell construction and LAMMPS workflow setup for high-throughput mechanical screening of molecular crystals.
  • Prepare periodic boundary condition simulations (shearing, compression) for organic materials without manual LAMMPS scripting.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Automates simulation workflows for organic crystal mechanical properties by generating structural models and force fields, then orchestrating LAMMPS-based molecular dynamics simulations under mechanical loads.

Yes, if you are a computational materials scientist working on organic crystal mechanics and already have LAMMPS installed with required modules. The low install friction and permissive MIT license are favorable, but the aging maintenance status (251 days since last release, 3 repository stars) and unspecified Python version support mean you should verify compatibility with your environment and be prepared for limited community support.

Install

pyocse on PyPI

pip

pip install pyocse

uv

uv add pyocse

poetry

poetry add pyocse

Installing pyocse

Before you install

Low install friction with only 2 runtime dependencies (toml, xmltodict). Maintenance status is aging—last commit was 2025-07-12 and 251 days since last release—so expect limited active support.

License in practice

MIT license permits commercial and private use with minimal restrictions, requiring only license and copyright notice retention.

Quickstart

pip install pyocse
from pyocse.build import Builder
import toml
import xmltodict

LAMMPS must be pre-installed and compiled with MOLECULE, EXTRA-MOLECULE, and KSPACE modules; conda environment setup required per documentation.

Verify before relying

  • Whether ambertools or openff integration is optional or required for core functionality
  • Minimum Python version and dependency version constraints not specified in metadata
  • Whether LAMMPS must be pre-installed and configured separately or if the package handles setup

Package facts

License MIT (permissive)
Python support not specified
Install friction low — pure-Python wheel
Runtime dependencies 2 — toml, xmltodict
Maintenance aging — 251 days since the last release
Last repo commit
First released
Downloads 563,058/month — #5,983 on PyPI (30-day window, as of 2026-08-14)
Known vulnerabilities none known (OSV.dev, checked 2026-08-14)

Evidence: pyocse-0.1.3-py3-none-any.whl

License :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3

Tags

organic crystal simulationmolecular dynamics workflow automationcrystal mechanical propertiesforce field generationLAMMPS crystal simulation
molecular-dynamicsmaterials-sciencehigh-throughput-simulation

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