ArticlePhysical chemistry chemical physics : PCCP2022
High-dimensional neural network potentials for accurate vibrational frequencies: the formic acid dimer benchmark.
Article in Physical chemistry chemical physics : PCCP, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 20 citations in OpenAlex.
- Vibrational spectra of formic acid and its dimer: I. Spectroscopic accuracy through matrix-isolation IR spectroscopy and anharmonic computations.Physical chemistry chemical physics : PCCP · 2026Article
- VPT2 Calculations of Vibrational Energies of CHThe journal of physical chemistry letters · 2026Article
- Δ-Machine Learning to Elevate DFT-Based Potentials and a Force Field to the CCSD(Journal of chemical theory and computation · 2024Article
- Rapid Characterization of Point Defects in Solid-State Ion Conductors Using Raman Spectroscopy, Machine-Learning Force Fields, and Atomic Raman Tensors.Journal of the American Chemical Society · 2024Article
- Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations.iScience · 2024Review
- Tensorial Properties via the Neuroevolution Potential Framework: Fast Simulation of Infrared and Raman Spectra.Journal of chemical theory and computation · 2024Article
- From the Automated Calculation of Potential Energy Surfaces to Accurate Infrared Spectra.The journal of physical chemistry letters · 2024Review
- High-dimensional neural network potentials for accurate vibrational frequencies: the formic acid dimer benchmark.Physical chemistry chemical physics : PCCP · 2022Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
In recent years, machine learning potentials (MLP) for atomistic simulations have attracted a lot of attention in chemistry and materials science. Many new approaches have been developed with the primary aim to transfer the accuracy of electronic structure calculations to large condensed systems containing thousands of atoms. In spite of these advances, the reliability of modern MLPs in reproducing the subtle details of the multi-dimensional potential-energy surface is still difficult to assess for such systems. On the other hand, moderately sized systems enabling the application of tools for thorough and systematic quality-control are nowadays rarely investigated. In this work we use benchmark-quality harmonic and anharmonic vibrational frequencies as a sensitive probe for the validation of high-dimensional neural network potentials. For the case of the formic acid dimer, a frequently studied model system for which stringent spectroscopic data became recently available, we show that high-quality frequencies can be obtained from state-of-the-art calculations in excellent agreement with coupled cluster theory and experimental data.
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