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 MOPAC executable provided here is licensed under LGPLv3, with sources available separately.
The models are licensed under the Academic Software Licence, enclosed in the models directory.
The PM6-ML method is described in the paper:
Nováček M., Řezáč J., J. Chem. Theory Comput. 2005. DOI: 10.1021/acs.jctc.4c01330
An earlier version is also available as a preprint.