MOPAC-ML implements the PM6-ML method, a semiempirical quantum-mechanical computational method that augments PM6 with a machine learning (ML) correction. It acts as a wrapper calling a modified version of MOPAC, to which it provides the ML correction.

MOPAC-ML has been developed for Linux and may not work on other platforms.

MOPAC-ML is used the same way as MOPAC, by running it with the name of the MOPAC input file as the single argument. Assuming that the MOPAC-ML directory is added to the PATH environment variable, it is as simple as:

mopac_ml mopac_input.in

The input is a standard MOPAC input file where the method is set to PM6 and an additional keyword MLCORR is added. An example of the input file can be found in the tests/01-standalone_mopac-ml directory.

Only one MOPAC-ML calculation can be run in one directory at the same time.

First, a modified version of MOPAC with the interface communicating with the wrapper is needed. Its source code is available as the pm6-ml branch of my fork of the MOPAC repository. Here, we provide a static binary for Linux compiled from that code. This binary must be placed in the same directory as the mopac-ml script, which is the case if you clone this repository.

Next, the Python environment for the ML correction needs to be set up. This can be easily done with conda:

conda create --name pm6-ml
conda activate pm6-ml
conda install -c conda-forge torchmd-net simple-dftd3 dftd3-python

The models directory contains the ML models. The default model used in the version of PM6-ML published in the preprint referenced below is the PM6-ML_correction_seed8_best.ckpt file. Four more models which ranked next in our selection are also provided but not used my MOPAC-ML.

This repository contain also the model files for the standalone MD potential discussed in the paper and trained in the same way as PM6-ML. These are not used by MOPAC-ML. The files are named TorchMD-NET-ET_standalone_*.

MOPAC-ML is licensed under the same license as MOPAC, which is LGPLv3.

The models are licensed under the Academic Software Licence, enclosed in the models directory.

The PM6-ML method is described in the preprint:

Nováček M, Řezáč J. PM6-ML: The Synergy of Semiempirical Quantum Chemistry and Machine Learning Transformed into a Practical Computational Method. ChemRxiv. 2024; doi:10.26434/chemrxiv-2024-3nwwv-v2