mcal Documentation¶

Overview¶

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

Features¶

Read crystal structures from CIF files
Quantum chemical calculations using Gaussian 09/16 or PySCF
Calculation of transfer integrals
Calculation of reorganization energy
Computation of mobility tensors and eigenvalues

Requirements¶

Python 3.9 or newer
NumPy
Pandas
Matplotlib
yu-tcal==4.0.3

Quantum Chemistry Calculation Tools¶

At least one of the following is required:

Gaussian 09 or 16
PySCF (macOS / Linux / WSL2(Windows Subsystem for Linux))
GPU4PySCF (macOS / Linux / WSL2(Windows Subsystem for Linux))

Important

When using Gaussian, the path to Gaussian must be set in your environment. PySCF is supported on macOS / Linux. Windows users must use WSL2.

Installation¶

Using Gaussian 09 or 16 (without PySCF)¶

pip install yu-mcal

Using PySCF (CPU only, macOS / Linux / WSL2)¶

pip install "yu-mcal[pyscf]"

Using GPU acceleration with PySCF (macOS / Linux / WSL2)¶

Check your installed CUDA Toolkit version:

nvcc --version

Install mcal with GPU acceleration:

If your CUDA Toolkit version is 12.x:

pip install "yu-mcal[gpu4pyscf-cuda12]"

If your CUDA Toolkit version is 11.x:

pip install "yu-mcal[gpu4pyscf-cuda11]"

Verify Installation¶

After installation, you can verify by running:

mcal --help

mcal Usage Manual¶

Basic Usage¶

mcal <cif_filename or pkl_filename> <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 both Gaussian and PySCF 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

PySCF Settings¶

`--pyscf`¶

Use PySCF instead of Gaussian for all calculations. Requires yu-mcal[pyscf].

Example: mcal xxx.cif p --pyscf

`--gpu4pyscf`¶

Use GPU acceleration via gpu4pyscf. Automatically enables PySCF mode (no need to specify --pyscf). Requires yu-mcal[gpu4pyscf-cuda11] or yu-mcal[gpu4pyscf-cuda12].

Example: mcal xxx.cif p --gpu4pyscf

`--cart`¶

Use Cartesian basis functions instead of spherical harmonics (PySCF only).

Example: mcal xxx.cif p --pyscf --cart

Calculation Control¶

`-r, --read`¶

Read results from existing files without executing calculations. With Gaussian, reads from log files; with PySCF, reads from checkpoint (.chk) files.

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. With Gaussian, checks log file termination; with PySCF, checks for existing checkpoint (.chk) files.

Example: mcal xxx.cif p --resume

`--fullcal`¶

Disable all speedup processing and calculate transfer integrals for all pairs from scratch. The following two optimizations are disabled:

Pair screening: pairs are normally skipped based on moment of inertia and center-of-mass distance; --fullcal disables this screening.
Monomer caching: monomer SCF calculations for the same molecule type are normally skipped by reusing a previously computed result file; --fullcal forces all monomer calculations to be performed from scratch.

Example: mcal xxx.cif p --fullcal

`--no-monomer-cache`¶

Disable only monomer caching. Pair screening remains active. All monomer SCF calculations are performed from scratch instead of reusing previously computed result files. When performing detailed transfer integral analysis using tcal, it is recommended to use this option.

Example: mcal xxx.cif p --no-monomer-cache

`--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 5x5x5 supercell)
Examples:
- mcal xxx.cif p --cellsize 1 (creates 3x3x3 supercell)
- mcal xxx.cif p --cellsize 3 (creates 7x7x7 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)"

PySCF Calculations¶

# Calculate using PySCF (CPU)
mcal xxx.cif p --pyscf

# Calculate using PySCF with GPU acceleration (no --pyscf needed)
mcal xxx.cif p --gpu4pyscf

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

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

# Read from existing PySCF checkpoint files
mcal xxx.cif p --pyscf -r

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

Generated Files¶

Reorganization Energy Files¶

The following files are generated during reorganization energy calculation (c = cation for p-type, a = anion for n-type):

Gaussian:

xxx_opt_n.gjf / xxx_opt_n.log — geometry optimization of neutral molecule
xxx_c.gjf / xxx_c.log (or xxx_a) — SP energy of ion at neutral geometry
xxx_opt_c.gjf / xxx_opt_c.log (or xxx_opt_a) — geometry optimization of ion
xxx_n.gjf / xxx_n.log — SP energy of neutral at ion geometry

PySCF:

xxx_opt_n.xyz / xxx_opt_n.chk — geometry optimization of neutral molecule
xxx_c.chk (or xxx_a.chk) — SP energy of ion at neutral geometry
xxx_opt_c.xyz / xxx_opt_c.chk (or xxx_opt_a) — geometry optimization of ion
xxx_n.chk — SP energy of neutral at ion geometry

Transfer Integral Files¶

mcal generates calculation files named using the (s_t_i_j_k) notation:

Symbol	Meaning
`s`	Molecule index in the reference unit cell (0,0,0)
`t`	Molecule index in the neighboring unit cell
`i`	Translation index along the a-axis
`j`	Translation index along the b-axis
`k`	Translation index along the c-axis

Example: xxx-(0_0_1_0_0) represents the transfer integral between the 0th molecule in the (0,0,0) cell and the 0th molecule in the (1,0,0) cell.

Gaussian:

xxx-(s_t_i_j_k).gjf / xxx-(s_t_i_j_k).log — dimer
xxx-(s_t_i_j_k)_m1.gjf / xxx-(s_t_i_j_k)_m1.log — monomer 1
xxx-(s_t_i_j_k)_m2.gjf / xxx-(s_t_i_j_k)_m2.log — monomer 2

PySCF:

xxx-(s_t_i_j_k).xyz / xxx-(s_t_i_j_k).chk — dimer
xxx-(s_t_i_j_k)_m1.chk — monomer 1
xxx-(s_t_i_j_k)_m2.chk — monomer 2

Notes¶

Note

Calculation Time: Calculation time varies significantly depending on the number of molecules and cell size. By default, two speedup mechanisms are enabled: pair pre-screening (skipping pairs unlikely to have significant transfer integrals) and monomer caching (reusing the isolated-molecule SCF result for molecule types already computed). Use --fullcal to disable both.
Memory Usage: Ensure sufficient memory for large systems
Gaussian Installation: Gaussian 09 or Gaussian 16 is required
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

API Reference¶

API Reference

mcal
- mcal package

Indices and Tables¶

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 @)

License¶

This project is released under the MIT License.

For more details, see the LICENSE file on GitHub.

Acknowledgements¶

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

mcal Documentation¶

Overview¶

Features¶

Requirements¶

Quantum Chemistry Calculation Tools¶

Installation¶

Using Gaussian 09 or 16 (without PySCF)¶

Using PySCF (CPU only, macOS / Linux / WSL2)¶

Using GPU acceleration with PySCF (macOS / Linux / WSL2)¶

Verify Installation¶

mcal Usage Manual¶

Basic Usage¶

Required Arguments¶

Basic Examples¶

Options¶

Calculation Settings¶

-M, --method <method>¶

-c, --cpu <number>¶

-m, --mem <memory>¶

-g, --g09¶

PySCF Settings¶

--pyscf¶

--gpu4pyscf¶

--cart¶

Calculation Control¶

-r, --read¶

-rp, --read_pickle¶

--resume¶

--fullcal¶

--no-monomer-cache¶

--cellsize <number>¶

Output Settings¶

-p, --pickle¶

--plot-plane <plane>¶

Practical Usage Examples¶

Basic Calculations¶

High-Precision Calculations¶

PySCF Calculations¶

Reusing Results¶

Output¶

Standard Output¶

Generated Files¶

Reorganization Energy Files¶

Transfer Integral Files¶

Notes¶

Troubleshooting¶

If calculation stops midway¶

Memory shortage error¶

To reduce calculation time¶

API Reference¶

Indices and Tables¶

Authors¶

License¶

Acknowledgements¶

`-M, --method <method>`¶

`-c, --cpu <number>`¶

`-m, --mem <memory>`¶

`-g, --g09`¶

`--pyscf`¶

`--gpu4pyscf`¶

`--cart`¶

`-r, --read`¶

`-rp, --read_pickle`¶

`--resume`¶

`--fullcal`¶

`--no-monomer-cache`¶

`--cellsize <number>`¶

`-p, --pickle`¶

`--plot-plane <plane>`¶