Theoretical Investigations of Rate Coefficients for H + O3 and HO2 + O Reactions on a Full-Dimensional Potential Energy Surface

Role of non-statistical effects in deciding the fate of HO3˙ in the atmosphere

HO3˙ has been postulated as a reservoir for OH˙ in various atmospheric reactions. Under collision-free conditions, experiments indicate that the lifetime of this species should be more than one microsecond. Interestingly, the binding energy of HO3˙ is estimated to be ∼3 kcal mol-1 by recent experimental as well as theoretical works. The value of the binding energy suggests that the lifetime of HO3˙ should be in the picosecond range, and with this lifetime, HO3˙ cannot act as a reservoir for OH˙. In the present work, using on-the-fly semiclassical dynamics, we argue that if non-RRKM effects are included, the lifetime of HO3˙ may be higher than that estimated from the binding energy.

Full-dimensional coupled-channel statistical approach to atom-triatom systems and applications to H/D + O3 reaction

The statistical quantum model (SQM), which assumes that the reactivity is controlled by entrance/exit channel quantum capture probabilities, is well suited for chemical reactions with a long-lived intermediate complex. In this work, a time-independent coupled-channel implementation of the SQM approach is developed for atom-triatom systems in full dimensionality. As SQM treats the capture dynamics quantum mechanically, it is capable of handling quantum effects such as tunneling. A detailed study of the H/D + O3 capture dynamics was performed by applying the newly developed SQM method on an accurate global potential energy surface. Agreement with previous ring polymer molecular dynamics (RPMD) results on the same potential energy surface is excellent except for very low temperatures. The SQM results are also in reasonably good agreement with available experimental rate coefficients. The strong H/D kinetic isotope effect underscores the dominant role of quantum tunneling under an entrance channel barrier at low temperatures.

Quasi-Classical Trajectory Calculation of Rate Constants Using an Ab Initio Trained Machine Learning Model (aML-MD) with Multifidelity Data

Machine learning (ML) provides a great opportunity for the construction of models with improved accuracy in classical molecular dynamics (MD). However, the accuracy of a ML trained model is limited by the quality and quantity of the training data. Generating large sets of accurate ab initio training data can require significant computational resources. Furthermore, inconsistent or incompatible data with different accuracies obtained using different methods may lead to biased or unreliable ML models that do not accurately represent the underlying physics. Recently, transfer learning showed its potential for avoiding these problems as well as for improving the accuracy, efficiency, and generalization of ML models using multifidelity data. In this work, ab initio trained ML-based MD (aML-MD) models are developed through transfer learning using DFT and multireference data from multiple sources with varying accuracy within the Deep Potential MD framework. The accuracy of the force field is demonstrated by calculating rate constants for the H + HO2 → H2 + 3O2 reaction using quasi-classical trajectories. We show that the aML-MD model with transfer learning can accurately predict the rate constants while reducing the computational cost by more than five times compared to the use of more expensive quantum chemistry training data sets. Hence, the aML-MD model with transfer learning shows great potential in using multifidelity data to reduce the computational cost involved in generating the training set for these potentials.

An Accurate Density Coherence Functional for Hybrid Multiconfiguration Density Coherence Functional Theory

We present hybrid multiconfiguration density coherence functional theory (HMC-DCFT), and we optimize a density coherence functional by parametrization against a diverse data set of 59 bond energies and 60 barrier heights. We compare the results to calculations on the same data set by CASSCF, CASPT2, six Kohn-Sham and hybrid Kohn-Sham exchange-correlation functionals, and three on-top functionals for pair-density functional theory (PDFT) and hybrid PDFT. The new functional has better accuracy than all compared methods.

Reaction Pathway Control via Reactant Vibrational Excitation and Impact on Product Vibrational Distributions: The O + HO2 → OH + O2 Atmospheric Reaction

Chemical reactions often have multiple pathways, the control of which is of fundamental and practical importance. In this Letter, we examine the dynamics of the O + HO₂ → OH + O₂ reaction, which plays an important role in atmospheric chemistry, using quasi-classical trajectories on a recently developed full-dimensional potential energy surface (PES). This reaction has two pathways leading to the same products: the H abstraction pathway (O_a + HO_bO_c → O_aH + O_bO_c) and the O abstraction pathway (O_a + HO_bO_c → O_bH + O_aO_c). Under thermal conditions, the reaction is dominated by the latter channel, which is barrierless, leading to vibrational excitation of the O₂ product. However, we demonstrate that excitation of the HO₂ reactant in its O-H (v₁) vibrational mode results in dramatic switching of the reaction pathway to the activated H abstraction channel, which leads to a highly excited OH product vibrational state distribution. The implications of such dynamical effects in the atmospheric chemistry are discussed.

Improved Computational Modeling of the Kinetics of the Acetylperoxy + HO2 Reaction

The acetylperoxy + HO2 reaction has multiple impacts on the troposphere, with a triplet pathway leading to peracetic acid + O2 (reaction 1a) competing with singlet pathways leading to acetic...

DFT-Machine Learning Approach for Accurate Prediction of pKa

In this study, we propose a novel method of pK_a prediction in a diverse set of acids, which combines density functional theory (DFT) method with machine learning (ML) methods. First, the DFT method with B3LYP/6-31++G**/SM8 is used to predict pK_a, yielding a mean absolute error of 1.85 pK_a units. Subsequently, such pK_a values predicted from the DFT method are employed as one of 10 molecular descriptors for developing ML models trained on experimental data. Kernel Ridge Regression (KRR), Gaussian Process Regression, and Artificial Neural Network are optimized using three Pipelines: Pipeline 1 involving only hyperparameter optimization (HPO), Pipeline 2 involving HPO followed by a relative contribution analysis (RCA) and recursive feature elimination (RFE), and Pipeline 3 involving HPO followed by RCA and RFE on an expanded set of composite features. Finally, it is demonstrated that KRR with Pipeline 3 yields optimal pK_a prediction at an MAE of 0.60 log units. This algorithm was then utilized to predict the pK_a of 37 novel acids. The two most important features were determined to be the number of hydrogen atoms in the molecule and the degree of oxidation of the acid. The predicted pK_a values were documented for future reference.

Application of Reaction Path Search Calculations to Potential Energy Surface Fits

There has been significant progress in recent years in the use of machine learning techniques to model high-dimensional reactive potential energy surfaces using large-scale data obtained from ab initio electronic structure calculations. In these methods, the strategy used to gather data becomes a key issue as the molecular size increases. In this work, we examine the applicability of the reaction path search algorithm implemented in the Global Reaction Route Mapping (GRRM) code as a data-gathering approach. The electronic energies and gradients sampled by using the GRRM calculation are directly used in potential energy surface fitting to a permutationally invariant polynomial function. This simple approach was applied to the HNS and HCNO reaction systems, and we found that the fitted potential energy surfaces reasonably reproduce the features of the electronic structure calculations used in the GRRM calculations. This suggests that the GRRM sampling scheme can be used to construct an initial potential energy surface.

Insights into the Formation of Hydroxyl Radicals with Nonthermal Vibrational Excitation in the Meinel Airglow

To understand night time airglow in the Meinel bands and heat conversion from the highly excited OH radicals in the upper atmosphere via the important atmospheric reaction H + O₃ → OH + O₂, we report here a quasi-classical trajectory study of the reaction dynamics on a recently developed full-dimensional potential energy surface (PES). Our results indicate that the reaction energy of this highly exoergic reaction is almost exclusively channeled into the vibration of the OH product, underscoring an extreme departure from the statistical limit. The calculated OH vibrational distribution is highly inverted and peaks near the highest accessible vibrational state, in excellent agreement with experimental observations, validating the accuracy of the PES. More importantly, the dynamical origin of the nonthermal excitation of the OH vibrational mode is identified by its large projection onto the reaction coordinate at a small potential barrier in the entrance channel, which controls the energy flow into various degrees of freedom in the products.

Theoretical H + O3 rate coefficients from ring polymer molecular dynamics on an accurate global potential energy surface: assessing experimental uncertainties

Thermal rate coefficients and kinetic isotope effects have been calculated for an important atmospheric reaction H/D + O3 → OH/OD + O2 based on an accurate permutation invariant polynomial-neural network potential energy surface, using ring polymer molecular dynamics (RPMD), quasi-classical trajectory (QCT) and variational transition-state theory (VTST) with multidimensional tunneling. The RPMD approach yielded results that are generally in better agreement with experimental rate coefficients than the VTST and QCT ones, especially at low temperatures, attributable to its capacity to capture quantum effects such as tunneling and zero-point energy. The theoretical results support one group of existing experiments over the other. In addition, rate coefficients for the D + O3 → OD + O2 reaction are also reported using the same methods, which will allow a stringent assessment of future experimental measurements, thus helping to reduce the uncertainty in the recommended rate coefficients of this reaction.

Advances and New Challenges to Bimolecular Reaction Dynamics Theory

Dynamics of bimolecular reactions in the gas phase are of foundational importance in combustion, atmospheric chemistry, interstellar chemistry, and plasma chemistry. These collision-induced chemical transformations are a sensitive probe of the underlying potential energy surface(s). Despite tremendous progress in past decades, our understanding is still not complete. In this Perspective, we survey the recent advances in theoretical characterization of bimolecular reaction dynamics, stimulated by new experimental observations, and identify key new challenges.

Neural Network Potential Energy Surfaces for Small Molecules and Reactions

We review progress in neural network (NN)-based methods for the construction of interatomic potentials from discrete samples (such as ab initio energies) for applications in classical and quantum dynamics including reaction dynamics and computational spectroscopy. The main focus is on methods for building molecular potential energy surfaces (PES) in internal coordinates that explicitly include all many-body contributions, even though some of the methods we review limit the degree of coupling, due either to a desire to limit computational cost or to limited data. Explicit and direct treatment of all many-body contributions is only practical for sufficiently small molecules, which are therefore our primary focus. This includes small molecules on surfaces. We consider direct, single NN PES fitting as well as more complex methods that impose structure (such as a multibody representation) on the PES function, either through the architecture of one NN or by using multiple NNs. We show how NNs are effective in building representations with low-dimensional functions including dimensionality reduction. We consider NN-based approaches to build PESs in the sums-of-product form important for quantum dynamics, ways to treat symmetry, and issues related to sampling data distributions and the relation between PES errors and errors in observables. We highlight combinations of NNs with other ideas such as permutationally invariant polynomials or sums of environment-dependent atomic contributions, which have recently emerged as powerful tools for building highly accurate PESs for relatively large molecular and reactive systems.