Implementation of the temperature transferable neural force field from our paper https://arxiv.org/abs/2007.14144

The model is based on SchNet [1-4]. It provides an interface to train and evaluate neural networks for force fields, specifically tested on coarse-grained ionic liquid simulations

Three notebooks run through the workflow of the paper

• Part1_cg_mapping.ipynb Uses coarse-grained auto-encoders for determening the best mapping for the ionic liquid. Uses MD data from LAMMPS with a force field for ionic liquids.
• Part2_data_file_creation.ipynb Using the previously mentioned data and the newly generated coarse-grained mapping preparing the data for training, applying the coarse-grained filter for the training data.
• Part3_temp_transfer.ipynb Training the model and running MD simulations on ASE. The hyperparameters in the model are the ones from the best run, though the data is not the full dataset. The best model from the paper is included in the ./examples/models/ directory

This software requires the following packages:

• PyTorch=1.4
• scikit-learn
• ase=3.18
• networkx=2.3

We highly recommend to create a conda environment to run the code. To do that, use the following commands:

conda upgrade conda
conda create -n nff python=3.7 scikit-learn pytorch>=1.2.0 cudatoolkit=10.0 ase pandas pymatgen -c pytorch -c conda-forge

You need to activate the tnff environment to install the NFF package:

conda activate nff

Finally, install the tnff package by running:

pip install .

• [1] K.T. Schütt. F. Arbabzadah. S. Chmiela, K.-R. Müller, A. Tkatchenko.
  Quantum-chemical insights from deep tensor neural networks. Nature Communications 8. 13890 (2017)
  10.1038/ncomms13890

• [2] K.T. Schütt. P.-J. Kindermans, H. E. Sauceda, S. Chmiela, A. Tkatchenko, K.-R. Müller.
  SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. Advances in Neural Information Processing Systems 30, pp. 992-1002 (2017) link

• [3] K.T. Schütt. P.-J. Kindermans, H. E. Sauceda, S. Chmiela, A. Tkatchenko, K.-R. Müller.
  SchNet - a deep learning architecture for molecules and materials. The Journal of Chemical Physics 148(24), 241722 (2018) 10.1063/1.5019779

• [4] K.T. Schütt, P. Kessel, M. Gastegger, K. Nicoli, A. Tkatchenko, K.-R. Müller. SchNetPack: A Deep Learning Toolbox For Atomistic Systems. J. Chem. Theory Comput. 15(1), 448-455 (2019). 10.1021/acs.jctc.8b00908