PercolateMAC

This code estimates the electrical conductances of ensembles of amorphous monolayer carbon (AMC) structures using percolation theory.

It was used to obtain the results described in this paper: https://arxiv.org/abs/2411.18041. Check it out (and its Supporting Information) for more details on the theory behind this code and its intended use.

The file requirements.txt contains the list of dependencies required to run this code. It can be used to construct a virtual environment using something like: virtualenv --no-download percolate_mac_env source percolate_mac_env/env/bin/activate pip install --no-index --upgrade pip pip install --no-index -r requirements.txt Many of the helper modules import functions from qcnico, which is another Python module not included in requirements.txt. The code for qcnico can be found here: https://github.com/ngastellu/qcnico.

How to run this code

The idea behind our approach is to estimate the ensemble-averaged conductance of a set of many AMC structures (see abovementioned paper for details), whose electronic structure has already been partially obtained using ArpackMAC.

Before running this code you therefore need: * A set of .xyz files containing the atomic positions of an ensemble AMC structures. All files corresponding to a given ensemble need to be stored in the same directory, and must be named according the following convention: <xyz_prefix>-<n>.xyz, where <xyz_prefix> can be defined in deploy_percolate.load_atomic_positions and n is an integer indexing each AMC structure in the ensemble. * The NPY files (tight-binding molecular orbitals/energies) produced by ArpackMAC , from the structures of the ensemble of interest. Do not rename any of the files produced by ArpackMAC. The calculation of an AMC ensemble's conductance is then done in two steps: 1. Construct the variable range hopping networks for each structure by running full_run_percolate.py on each structure in the ensemble. 2. Use the distribution of critical percolation thresholds in the ensemble to calculate its conductance, with obtain_conductances.py. For more information on these two scripts, see the Contents section below.

N.B.: full_run_percolate.py only processes a single file, and so must be invoked in run separately for each structure in the ensemble. On the otherobtain_conductivities.py loops over all structures, and thus only needs to be run once per ensemble.

Contents

Constructing the hopping conductance networks

The main script is full_run_percolate.py. It constructs the hopping sites from the tight-binding eigenstates obtained by the code in ArpackMAC, and builds the conductance networks between them edge by edge, until a network connects the left edge of the structure to the right edge.

The script takes three command line arguments: * n: an integer indexing the specific AMC structure to run the percolation calculation on * struc_type: the name of the ensemble to which the AMC structure being considered belongs. Three valid values: sAMC-300, sAMC-q400, and sAMC-500. * mo_type: The type of tight-binding eigenstate used to construct the hopping sites and run the percolation calculation. Three valid values: lo (lowest-energy eigenstates), hi (highest energy eigenstates), and virtual_w_HOMO (low-lying unoccupied eigenstates).

Computing the conductance of an ensemble.

Once the percolation networks have been constructed for all of the desired structures in a given ensemble, use the obtain_conductances.py script to obtain the ensemble's electrical conductances from the distribution of critical percolation distances in that ensemble:

$$G = \frac{qe^2\omega0}{k{\text{B}}T}\,\int e^{-\xi}\,P(\xi)\,\text{d}\xi\,.$$ The values for the escape frequency $\omega0$ and the temperature can be set at the top utils_analysis.py. The script accepts a single command line argument: mo_type, which is the same as the mo_type argument for full_run_percolate.py.

Other modules

The other Python files are helper modules that contain the code doing the actual computations. Here is a brief description of their purpose: * MOs2sites.py: Handles the mapping from molecular orbitals to variable range hopping sites * percolate.py: Defines hopping rates between pairs of sites and constructs the hopping conduction networks. * utils_arpackMAC.py: Processes the output from theArpackMAC code used to obtain the tight-binding eigenstates of each AMC structure. * utils_analysis.py: Processes the results of the percolation calculation run by percolate to obtain the conductance of a given AMC ensemble.

Some usage notes

The code expects the output of the electronic structure calculation (tight-binding eigenstates, and eigenvalues) to be stored in NPY files with same names as the one produced by the ArpackMAC software package (https://github.com/ngastellu/ArpackMAC). However, this can be remedied by appropriately modifying the data loading function load_arpack_data in deploy_percolate.py.

This code was developed and ran on the Narval HPC cluster operated by the Digital Research Alliance of Canada.

Citing this code

Please cite the following paper in any work using this code: https://arxiv.org/abs/2411.18041

Gastellu, Nicolas, Ata Madanchi, and Lena Simine. "Disentangling morphology and conductance in amorphous graphene." arXiv preprint arXiv:2411.18041 (2024).