ArticleJournal of chemical theory and computation2024
Tensorial Properties via the Neuroevolution Potential Framework: Fast Simulation of Infrared and Raman Spectra.
Article in Journal of chemical theory and computation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- qNEP: A Highly Efficient Neuroevolution Potential with Dynamic Charges for Large-Scale Atomistic Simulations.Journal of chemical theory and computation · 2026Article
- Multiscale Machine Learning Prediction of Infrared Spectra of Solvated Molecules.Journal of chemical theory and computation · 2026Article
- Decomposition Analysis of Theoretical Raman Spectra for Efficient Interpretation of Experimental Spectra of Thin-Film Functional Materials.International journal of molecular sciences · 2025Article
- Refining potential energy surface through dynamical properties via differentiable molecular simulation.Nature communications · 2025Article
- Efficient Composite Infrared Spectroscopy: Combining the Double-Harmonic Approximation with Machine Learning Potentials.Journal of chemical theory and computation · 2024Article
- E(n)-Equivariant cartesian tensor message passing interatomic potential.Nature communications · 2024Article
- Machine Learning for Polaritonic Chemistry: Accessing Chemical Kinetics.Journal of the American Chemical Society · 2024Article
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10 authors.
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Abstract
Infrared and Raman spectroscopy are widely used for the characterization of gases, liquids, and solids, as the spectra contain a wealth of information concerning, in particular, the dynamics of these systems. Atomic scale simulations can be used to predict such spectra but are often severely limited due to high computational cost or the need for strong approximations that limit the application range and reliability. Here, we introduce a machine learning (ML) accelerated approach that addresses these shortcomings and provides a significant performance boost in terms of data and computational efficiency compared with earlier ML schemes. To this end, we generalize the neuroevolution potential approach to enable the prediction of rank one and two tensors to obtain the tensorial neuroevolution potential (TNEP) scheme. We apply the resulting framework to construct models for the dipole moment, polarizability, and susceptibility of molecules, liquids, and solids and show that our approach compares favorably with several ML models from the literature with respect to accuracy and computational efficiency. Finally, we demonstrate the application of the TNEP approach to the prediction of infrared and Raman spectra of liquid water, a molecule (PTAF
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