# Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra

arXiv: https://arxiv.org/abs/1705.05907 · HTML: https://mlsanity.com/paper/1705.05907/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2017.

## Built on by (most cited first, 30 of 30)

- [Equivariant message passing for the prediction of tensorial properties and molecular spectra](https://mlsanity.com/paper/2102.03150/) (2021)
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- [Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials](https://mlsanity.com/paper/2312.01416/) (2023)
- [Transfer learning for chemically accurate interatomic neural network potentials](https://mlsanity.com/paper/2212.03916/) (2022)
- [Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments](https://mlsanity.com/paper/2109.09569/) (2021)
- [Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations](https://mlsanity.com/paper/2205.08306/) (2022)
- [Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond](https://mlsanity.com/paper/2502.09897/) (2025)
- [Learning Non-Local Molecular Interactions via Equivariant Local Representations and Charge Equilibration](https://mlsanity.com/paper/2501.19179/) (2025)
- [LTAU-FF: Loss Trajectory Analysis for Uncertainty in Atomistic Force Fields](https://mlsanity.com/paper/2402.00853/) (2024)
- [Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space](https://mlsanity.com/paper/2204.09831/) (2022)
- [SpectrumWorld: Artificial Intelligence Foundation for Spectroscopy](https://mlsanity.com/paper/2508.01188/) (2025)
- [Scalable Machine Learning Force Fields for Macromolecular Systems Through Long-Range Aware Message Passing](https://mlsanity.com/paper/2601.03774/) (2026)
- [Detect the Interactions that Matter in Matter: Geometric Attention for Many-Body Systems](https://mlsanity.com/paper/2106.02549/) (2021)
- [From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fields](https://mlsanity.com/paper/2309.15126/) (2023)
- [Physics-informed active learning for accelerating quantum chemical simulations](https://mlsanity.com/paper/2404.11811/) (2024)
- [Advancing Machine Learning in Industry 4.0: Benchmark Framework for Rare-event Prediction in Chemical Processes](https://mlsanity.com/paper/2409.00485/) (2024)
- [A Universal Deep Learning Force Field for Molecular Dynamic Simulation and Vibrational Spectra Prediction](https://mlsanity.com/paper/2510.04227/) (2025)
- [Training Algorithm Matters for the Performance of Neural Network Potential: A Case Study of Adam and the Kalman Filter Optimizers](https://mlsanity.com/paper/2109.03769/) (2021)
- [Edge Direction-invariant Graph Neural Networks for Molecular Dipole Moments Prediction](https://mlsanity.com/paper/2206.12867/) (2022)
- [Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Water](https://mlsanity.com/paper/2312.01414/) (2023)
- [Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments](https://mlsanity.com/paper/2312.01415/) (2023)
- [Deep Neural Network for Phonon-Assisted Optical Spectra in Semiconductors](https://mlsanity.com/paper/2502.00798/) (2025)
- [Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy](https://mlsanity.com/paper/2507.06929/) (2025)
- [From Static Spectra to Operando Infrared Dynamics: Physics Informed Flow Modeling and a Benchmark](https://mlsanity.com/paper/2602.18551/) (2026)
- [Data Fusion and Contrastive Alignment for Unconstrained IR Molecular Structure Elucidation](https://mlsanity.com/paper/2607.26164/) (2026)
- [SpecCal: Ambiguity-Aware Candidate Calibration for Infrared Spectrum-Based Molecular Structure Reconstruction](https://mlsanity.com/paper/2607.27788/) (2026)

## Abstract (arXiv)

Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for vibrational anharmonic and dynamical effects -- typically neglected by conventional quantum chemistry approaches -- we base our machine learning strategy on ab initio molecular dynamics simulations. While these simulations are usually extremely time consuming even for small molecules, we overcome these limitations by leveraging the power of a variety of machine learning techniques, not only accelerating simulations by several orders of magnitude, but also greatly extending the size of systems that can be treated. To this end, we develop a molecular dipole moment model based on environment dependent neural network charges and combine it with the neural network potentials of Behler and Parrinello. Contrary to the prevalent big data philosophy, we are able to obtain very accurate machine learning models for the prediction of infrared spectra based on only a few hundreds of electronic structure reference points. This is made possible through the introduction of a fully automated sampling scheme and the use of molecular forces during neural network potential training. We demonstrate the power of our machine learning approach by applying it to model the infrared spectra of a methanol molecule, n-alkanes containing up to 200 atoms and the protonated alanine tripeptide, which at the same time represents the first application of machine learning techniques to simulate the dynamics of a peptide. In all these case studies we find excellent agreement between the infrared spectra predicted via machine learning models and the respective theoretical and experimental spectra.