ArticleJournal of the American Chemical Society2023
Ring-Polymer Instanton Tunneling Splittings of Tropolone and Isotopomers using a Δ-Machine Learned CCSD(T) Potential: Theory and Experiment Shake Hands.
Article in Journal of the American Chemical Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- The Different Levels of Quantum: A Methodological-Philosophical Portrait of Computational Quantum Chemistry.ACS physical chemistry Au · 2026Review
- Compact Kernel/Neural Network Representation for Accurate, Fast, and Global Reactive Molecular Potential Energy Surfaces.Precision chemistry · 2026Article
- Tests Demonstrate the Accuracy of theThe journal of physical chemistry. A · 2026Article
- "Gold-Standard" Δ-Machine Learned Transferable Potential for Linear Alkanes.The journal of physical chemistry letters · 2025Article
- Multicomponent Double-Hybrid Density Functional Theory.Journal of chemical theory and computation · 2025Article
- Dynamics of protonated oxalate from machine-learned simulations and experiment: infrared signatures, proton transfer dynamics and tunneling splittings.Physical chemistry chemical physics : PCCP · 2025Article
- Vibrational Spectroscopy Through Time Averaged Fourier Transform of Autocorrelated Molecular Dynamics Data: Introducing the Free SEMISOFT Web-Platform.Journal of computational chemistry · 2025Article
- Δ-Machine Learning to Elevate DFT-Based Potentials and a Force Field to the CCSD(Journal of chemical theory and computation · 2024Article
- Nuclear Quantum Effects in Proton or Hydrogen Transfer.The journal of physical chemistry letters · 2024Review
- Many-Body Methods for Surface Chemistry Come of Age: Achieving Consensus with Experiments.Journal of the American Chemical Society · 2023Article
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13 authors.
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Abstract
Tropolone, a 15-atom cyclic molecule, has received much interest both experimentally and theoretically due to its H-transfer tunneling dynamics. An accurate theoretical description is challenging owing to the need to develop a high-level potential energy surface (PES) and then to simulate quantum-mechanical tunneling on this PES in full dimensionality. Here, we tackle both aspects of this challenge and make detailed comparisons with experiments for numerous isotopomers. The PES, of near CCSD(T)-quality, is obtained using a Δ-machine learning approach starting from a pre-existing low-level DFT PES and corrected by a small number of approximate CCSD(T) energies obtained using the fragmentation-based molecular tailoring approach. The resulting PES is benchmarked against DF-FNO-CCSD(T) and CCSD(T)-F12 calculations. Ring-polymer instanton calculations of the splittings, obtained with the Δ-corrected PES are in good agreement with previously reported experiments and a significant improvement over those obtained using the low-level DFT PES. The instanton path includes heavy-atom tunneling effects and cuts the corner, thereby avoiding passing through the conventional saddle-point transition state. This is in contradistinction with typical approaches based on the minimum-energy reaction path. Finally, the subtle changes in the splittings for some of the heavy-atom isotopomers seen experimentally are reproduced and explained.
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