Skip to main content

Program for the calculation of mobility tensor for organic semiconductor crystals

Project description

mcal: Program for the calculation of mobility tensor for organic semiconductor crystals

Python License: MIT docs

Overview

mcal.py is a tool for calculating mobility tensors of organic semiconductors. It calculates transfer integrals and reorganization energy from crystal structures, and determines mobility tensors considering anisotropy and path continuity.

Requirements

  • Python 3.9 or newer
  • NumPy
  • Pandas
  • Matplotlib
  • yu-tcal==3.1.0
  • Gaussian 09 or 16

Important notice

  • The path of the Gaussian must be set.

Installation

pip install yu-mcal

Verify Installation

After installation, you can verify by running:

mcal --help

mcal Usage Manual

Basic Usage

mcal <cif_filename or pkl_filenname> <osc_type> [options]

Required Arguments

  • cif_filename: Path to the CIF file
  • pkl_filename: Path to the pickle file
  • osc_type: Organic semiconductor type
    • p: p-type semiconductor (uses HOMO level)
    • n: n-type semiconductor (uses LUMO level)

Basic Examples

# Calculate as p-type semiconductor
mcal xxx.cif p

# Calculate as n-type semiconductor
mcal xxx.cif n

Options

Calculation Settings

-M, --method <method>

Specify the calculation method used in Gaussian calculations.

  • Default: B3LYP/6-31G(d,p)
  • Example: mcal xxx.cif p -M "B3LYP/6-31G(d)"

-c, --cpu <number>

Specify the number of CPUs to use.

  • Default: 4
  • Example: mcal xxx.cif p -c 8

-m, --mem <memory>

Specify the amount of memory in GB.

  • Default: 10
  • Example: mcal xxx.cif p -m 16

-g, --g09

Use Gaussian 09 (default is Gaussian 16).

  • Example: mcal xxx.cif p -g

Calculation Control

-r, --read

Read results from existing log files without executing Gaussian.

  • Example: mcal xxx.cif p -r

-rp, --read_pickle

Read results from existing pickle file without executing calculations.

  • Example: mcal xxx_result.pkl p -rp

--resume

Resume calculation using existing results if log files terminated normally.

  • Example: mcal xxx.cif p --resume

--fullcal

Disable speedup processing using moment of inertia and distance between centers of weight, and calculate transfer integrals for all pairs.

  • Example: mcal xxx.cif p --fullcal

--cellsize <number>

Specify the number of unit cells to expand in each direction around the central unit cell for transfer integral calculations.

  • Default: 2 (creates 5×5×5 supercell)
  • Examples:
    • mcal xxx.cif p --cellsize 1 (creates 3×3×3 supercell)
    • mcal xxx.cif p --cellsize 3 (creates 7×7×7 supercell)

Output Settings

-p, --pickle

Save calculation results to a pickle file.

  • Example: mcal xxx.cif p -p

--plot-plane <plane>

Plot mobility tensor as a 2D polar plot on specified crystallographic plane.

  • Available planes: ab, ac, ba, bc, ca, cb
  • Default: None (no plot generated)
  • Examples:
    • mcal xxx.cif p --plot-plane ab (plot on ab-plane)
    • mcal xxx.cif p --plot-plane bc (plot on bc-plane)

Practical Usage Examples

Basic Calculations

# Calculate mobility of p-type xxx
mcal xxx.cif p

# Use 8 CPUs and 16GB memory
mcal xxx.cif p -c 8 -m 16

High-Precision Calculations

# Calculate transfer integrals for all pairs (high precision, time-consuming)
mcal xxx.cif p --fullcal

# Use larger supercell to widen transfer integral calculation range
mcal xxx.cif p --cellsize 3

# Use different basis set
mcal xxx.cif p -M "B3LYP/6-311G(d,p)"

Reusing Results

# Read from existing calculation results
mcal xxx.cif p -r

# Read from existing pickle file
mcal xxx_result.pkl p -rp

# Resume interrupted calculation
mcal xxx.cif p --resume

# Save results to pickle file
mcal xxx.cif p -p

Output

Standard Output

  • Reorganization energy
  • Transfer integrals for each pair
  • Diffusion coefficient tensor
  • Mobility tensor
  • Eigenvalues and eigenvectors of mobility

Notes

  1. Calculation Time: Calculation time varies significantly depending on the number of molecules and cell size
  2. Memory Usage: Ensure sufficient memory for large systems
  3. Gaussian Installation: Gaussian 09 or Gaussian 16 is required
  4. Dependencies: Make sure all required Python libraries are installed

Troubleshooting

If calculation stops midway

# Resume with --resume option
mcal xxx.cif p --resume

Memory shortage error

# Increase memory amount
mcal xxx.cif p -m 32

To reduce calculation time

# Enable speedup processing (default)
mcal xxx.cif p

# Use smaller supercell for faster calculation
mcal xxx.cif p --cellsize 1

# Increase number of CPUs
mcal xxx.cif p -c 16

Authors

Matsui Laboratory, Research Center for Organic Electronics (ROEL), Yamagata University
Hiroyuki Matsui, Koki Ozawa
Email: h-matsui[at]yz.yamagata-u.ac.jp
Please replace [at] with @

Acknowledgements

This work was supported by JSPS Grant-in-Aid for JSPS Fellows Grant Number JP25KJ0647.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

yu_mcal-0.3.1.tar.gz (349.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

yu_mcal-0.3.1-py3-none-any.whl (31.7 kB view details)

Uploaded Python 3

File details

Details for the file yu_mcal-0.3.1.tar.gz.

File metadata

  • Download URL: yu_mcal-0.3.1.tar.gz
  • Upload date:
  • Size: 349.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.12

File hashes

Hashes for yu_mcal-0.3.1.tar.gz
Algorithm Hash digest
SHA256 f3e406037ee86c9135ec0ca2bce19ec03eeab4ba6b38ea36071db34831da6b2b
MD5 8be154e1bf01ba162f598b20bd51afa8
BLAKE2b-256 8cf9716d8e37f8572a77ce675f0fa047266cca55872e5bdfa9f9ca4f848afff9

See more details on using hashes here.

File details

Details for the file yu_mcal-0.3.1-py3-none-any.whl.

File metadata

  • Download URL: yu_mcal-0.3.1-py3-none-any.whl
  • Upload date:
  • Size: 31.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.12

File hashes

Hashes for yu_mcal-0.3.1-py3-none-any.whl
Algorithm Hash digest
SHA256 f1066617d7925e8cecdad8fd0765aecb9510396d02cc5a387221a6b9ab4ff5e4
MD5 e4dd0d02fa5cbdf55a390adc95a6311f
BLAKE2b-256 954d64d3dd6b4777ddbc35db90a9451a163d4064e0bc08b506b3418542cf6701

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page