Supplementary data and code for 'Computing Anharmonic Infrared Spectra of Polycyclic Aromatic Hydrocarbons Using Machine-Learning Molecular Dynamics'

Supplementary data and code for 'Computing Anharmonic Infrared Spectra of Polycyclic Aromatic Hydrocarbons Using Machine-Learning Molecular Dynamics'

---------------------------Creator---------------------------Xinghong Mai et al.

---------------------------Description---------------------------This zip file contains code for calculating the anharmonic infrared (IR) spectrum of polycyclic aromatic hydrocarbons (PAHs) using a machine learning-based molecular dynamics (MLMD) approach. This zip file also includes the spectral data for the 1704 theoretically-calculated and 49 experimentally-tested PAHs as mentioned in the paper.

The MLMD approach employs two distinct machine learning (ML) models:

Neural Network Force Field (NNFF) [doi:10.1021/acs.jcim.1c01380]: Used to construct the potential energy surface.

Electron Passing Neural Network (EPNN) [doi:10.1021/acs.jcim.0c01071]: Used to predict the molecular dipole moment.

To compute the anharmonic IR spectrum, molecular dynamics (MD) simulations are performed to obtain atomic configurations (trajectories) during molecular vibrations. These configurations are generated using atomic forces predicted by the NNFF model. The resulting atomic trajectories are then used by the EPNN model to calculate the dipole moments at each time step. The dipole time-autocorrelation function is subjected to a Fourier transform to derive the IR intensity.

Both the NNFF and EPNN models are pre-trained and ready for immediate use, no additional ML training is required. 

---------------------------Citation---------------------------

For more information on the training of these models and the MD simulations, please refer to the following article:

Computing Anharmonic Infrared Spectra of Polycyclic Aromatic Hydrocarbons Using Machine-Learning Molecular DynamicsarXiv:2503.05120https://doi.org/10.48550/arXiv.2503.05120Authors: Xinghong Mai, Zhao Wang, Lijun Pan, Johannes Schorghuber, Peter Kovacs, Jesus Carrete, and Georg K. H. Madsen

If you use our data or code, please cite the above article and acknowledge the code in your publications. For inquiries, you can contact Prof. Zhao Wang at zw[at]gxu.edu.cn.

---------------------------License---------------------------The data is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0), which permits sharing and adaptation for non-commercial purposes, provided appropriate credit is given and any derivative works are distributed under the same license.

The code is licensed under the Apache License 2.0, allowing for free use, modification, and distribution, including for commercial purposes, while requiring proper attribution and inclusion of the original license.

---------------------------Instructions to use the code---------------------------

See the file readme.txt

---------------------------Data Contents---------------------------This ZIP file also contains spectral data for 1,704 theoretically calculated and 49 experimentally tested PAHs, located in the ./outputs directory.