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Machine learning quantum chemistry of potential energy surfaces
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Convergent ab initio analysis of the multi-channel HOBr + H reaction
Published in The Journal of Chemical Physics, 2024
High-level potential energy surfaces for three reactions of hypobromous acid with atomic hydrogen were computed at the CCSDTQ/CBS//CCSDT(Q)/complete basis set level of theory. Focal point analysis was utilized to extrapolate energies and gradients for energetics and optimizations, respectively. The H attack at Br and subsequent Br–O cleavage were found to proceed barrierlessly. The slightly submerged transition state lies −0.2 kcal mol−1 lower in energy than the reactants and produces OH and HBr. The two other studied reaction paths are the radical substitution to produce H2O and Br with a 4.0 kcal mol−1 barrier and the abstraction at hydrogen to produce BrO and H2 with an 11.2 kcal mol−1 barrier. The final product energies lie −37.2, −67.9, and −7.3 kcal mol−1 lower in energy than reactants, HOBr + H, for the sets of products OH + HBr, H2O + Br, and H2 + BrO, respectively. Additive corrections computed for the final energetics, particularly the zero-point vibrational energies and spin–orbit corrections, significantly impacted the final stationary point energies, with corrections up to 6.2 kcal mol−1.
Recommended citation: Beck, I. T.; Lahm, M. E.; Douberly, G. E.; Schaefer III, H. F. Convergent ab initio analysis of the multi-channel HOBr + H reaction. Journal of Chemical Physics 2024, 160(12), 124304
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Evaluating the Importance of Conformers for Understanding the Vacuum-Ultraviolet Spectra of Oxiranes: Experiment and Theory
Published in The Journal of Physical Chemistry A, 2024
Vacuum-ultraviolet (VUV) absorption spectroscopy enables electronic transitions that offer the unambiguous identification of molecules. As target molecules become more complex, multifunctional species present a great challenge to both experimental and computational spectroscopy. This research reports both experimental and theoretical studies of oxiranes. Computationally, the nuclear ensemble approach has been used to accurately predict experimental spectra for a variety of molecules. However, this approach incurs great computational cost, as ensembles generally consist of thousands of geometries. The present study aims to drastically reduce the ensemble by evaluating the significance of the conformers to the predicted spectra. This approach was applied to 11 substituted oxiranes using the Conformer Rotamer Ensemble Sampling Tool (CREST) of Grimme to generate an ensemble of unique conformers determined by their Boltzmann populations. Five TD-DFT functionals (BMK, CAM-B3LYP, M06-2X, MN15, ωB97X-D) and EOM-CCSD were used to simulate the spectrum of each substituted oxirane ensemble. Computed spectra were then compared to the experiment using both qualitative and quantitative metrics. Based on these metrics, it was observed that certain conformers may not be necessary to characterize this set of oxiranes despite the temperature (323 K) of the experiment. A single conformer can then be used with TD-DFT and EOM-CCSD to replicate the experimental spectra of these medium-sized combustion species.
Recommended citation: Beck, I. T.; Mitchell, E. C.; Webb Hill, A.; Turney, J. M.; Rotavera, B.; Schaefer III, H. F. Evaluating the Importance of Conformers for Understanding the Vacuum-Ultraviolet Spectra of Oxiranes: Experiment and Theory Journal of Physical Chemistry A 2024, 128(50), 10906-10920
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Methods in PES-Learn: Direct-Fit Machine Learning of Born–Oppenheimer Potential Energy Surfaces
Published in Molecules, 2025
The release of PES-Learn version 1.0 as an open-source software package for the automatic construction of machine learning models of semi-global molecular potential energy surfaces (PESs) is presented. Improvements to PES-Learn’s interoperability are stressed with new Python API that simplifies workflows for PES construction via interaction with QCSchema input and output infrastructure. In addition, a new machine learning method is introduced to PES-Learn: kernel ridge regression (KRR). The capabilities of KRR are emphasized with examination of select semi-global PESs. All machine learning methods available in PES-Learn are benchmarked with benzene and ethanol datasets from the rMD17 database to illustrate PES-Learn’s performance ability. Fitting performance and timings are assessed for both systems. Finally, the ability to predict gradients with neural network models is presented and benchmarked with ethanol and benzene. PES-Learn is an active project and welcomes community suggestions and contributions.
Recommended citation: Beck, I. T.; Turney, J. M.; Schaefer III, H. F. Methods in PES-Learn: Direct-Fit Machine Learning of Born–Oppenheimer Potential Energy Surfaces. Molecules 2025, 31(1), 100
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