Permutationally invariant fitting of intermolecular potential energy surfaces: A case study of the Ne-C2H2 system

Collisional Relaxation of Highly Vibrationally Excited Acetylene Mediated by the Vinylidene Isomer

Collisional relaxation of highly vibrationally excited acetylene, generated from the 193 nm photolysis of vinyl bromide with roughly 23,000 cm-1 of nascent vibrational energy, is studied via submicrosecond time-resolved Fourier transform infrared (FTIR) emission spectroscopy. IR emission from vibrationally hot acetylene during collisional relaxation by helium, neon, argon, and krypton rare-gas colliders is recorded and analyzed to deduce the acetylene energy content as a function of time. The average energy lost per collision, ⟨ΔE⟩, is computed using the Lennard-Jones collision frequency. Two distinct vibrational-to-translational (V-T) energy transfer regimes in terms of the acetylene energy are identified. At vibrational energies below 10,000-14,000 cm-1, energy transfer efficiency increases linearly with molecular energy content and is in line with typical V-T behavior in quantity. In contrast, above 10,000-14,000 cm-1, the V-T energy transfer efficiency displays a dramatic and rapid increase. This increase is nearly coincident with the acetylene-vinylidene isomerization limit, which occurs nearly 15,000 cm-1 above the acetylene zero-point energy. Combined quasi-classical trajectory calculations and Schwartz-Slawsky-Herzfeld-Tanczos theory point to a vinylidene contribution being responsible for the large enhancement. This observation illustrates the influence of energetically accessible structural isomers to greatly enhance the energy transfer rates of highly vibrationally excited molecules.

Efficient prediction for high precision CO-N2 potential energy surface by stacking ensemble DNN

High-dimensional potential energy surface (PES) for van der Waals systems with spectroscopic accuracy, is of great importance for quantum dynamics and an extremely challenge job. CO-N₂ is a typical van der Waals system and its high-precision PES may help elucidate weak interaction mechanisms. Taking CO-N₂ potential energies calculated by CCSD(T)-F12b/aug-cc-pVQZ as the benchmark, we establish an accurate, robust, and efficient machine learning model by using only four molecular structure descriptors based on 7966 benchmark potential energies. The highest accuracy is obtained by a stacking ensemble DNN (SeDNN). Its evaluation parameters MAE, RMSE, and R² reach 0.096, 0.163, 0.9999 cm^-1 , respectively, and the spectroscopic accuracy for vibration spectrum is achieved with predicted PES, which shows SeDNN superior goodness-of-fit and prediction performance. An elaborated PES with the reported global minimum has been predicted with the model, which perfectly reproduces CCSD(T) potential energies and the analytical MLR PES [PCCP, 2018, 20, 2036]. The critical points (global minimum, TSI, TSII, and their barriers), potential curve, and entire PES profile are remarkably consistent with CCSD(T) calculations. To further improve the usability of constructing PESs in practice, the size of the training set (energy points) for the model is reduced to 50%, 30%, and 20% of the database, respectively. The results show that even training with the smallest training set (1593 points), the PES only differs 2.555 cm^-1 with the analytic MLR PES. Therefore, the proposed SeDNN is promisingly an alternative efficient tool to construct subtle PES for van der Waals systems.

Computational vibrational spectroscopy of molecule-surface interactions: what is still difficult and what can be done about it

Interactions of molecules with solid surfaces are responsible for key functionalities for a range of currently actively pursued technologies, including heterogeneous catalysis for synthesis or decomposition of molecules, sensitization, surface...

Energy exchange rate coefficients from vibrational inelastic O2(Σg-3) + O2(Σg-3) collisions on a new spin-averaged potential energy surface

A new spin-averaged potential energy surface (PES) for non-reactive O₂(Σg-3) + O₂(Σg-3) collisions is presented. The potential is formulated analytically according to the nature of the principal interaction components, with the main van der Waals contribution described through the improved Lennard-Jones model. All the parameters involved in the formulation, having a physical meaning, have been modulated in restricted variation ranges, exploiting a combined analysis of experimental and ab initio reference data. The new PES is shown to be able to reproduce a wealth of different physical properties, ranging from the second virial coefficients to transport properties (shear viscosity and thermal conductivity) and rate coefficients for inelastic scattering collisions. Rate coefficients for the vibrational inelastic processes of O₂, including both vibration-to-vibration (V-V) and vibration-to-translation/rotation (V-T/R) energy exchanges, were then calculated on this PES using a mixed quantum-classical method. The effective formulation of the potential and its combination with an efficient, yet accurate, nuclear dynamics treatment allowed for the determination of a large database of V-V and V-T/R energy transfer rate coefficients in a wide temperature range.

Supercollisions of fast H-atom with ethylene on an accurate full-dimensional potential energy surface

The collisions transferring large portions of energy are often called supercollisions. In the H + C₂H₂ reactive system, the rovibrationally cold C₂H₂ molecule can be activated with substantial internal excitations by its collision with a translationally hot H atom. It is interesting to investigate the mechanisms of collisional energy transfer in other important reactions of H with hydrocarbons. Here, an accurate, global, full-dimensional potential energy surface (PES) of H + C₂H₄ was constructed by the fundamental invariant neural network fitting based on roughly 100 000 UCCSD(T)-F12a/aug-cc-pVTZ data points. Extensive quasi-classical trajectory calculations were carried out on the full-dimensional PES to investigate the energy transfer process in collisions of the translationally hot H atoms with C₂H₄ in a wide range of collision energies. The computed function of the energy-transfer probability is not a simple exponential decay function but exhibits large magnitudes in the region of a large amount of energy transfer, indicating the signature of supercollisions. The supercollisions among non-complex-forming nonreactive (prompt) trajectories are frustrated complex-forming processes in which the incoming H atom penetrates into C₂H₄ with a small C-H distance but promptly and directly leaves C₂H₄. The complex-forming supercollisions, in which either the attacking H atom leaves (complex-forming nonreactive collisions) or one of the original H atoms of C₂H₄ leaves (complex-forming reactive trajectories), dominate large energy transfer from the translational energy to internal excitation of molecule. The current work sheds valuable light on the energy transfer of this important reaction in the combustion and may motivate related experimental investigations.

Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation

Combustion is a complex chemical system which involves thousands of chemical reactions and generates hundreds of molecular species and radicals during the process. In this work, a neural network-based molecular dynamics (MD) simulation is carried out to simulate the benchmark combustion of methane. During MD simulation, detailed reaction processes leading to the creation of specific molecular species including various intermediate radicals and the products are intimately revealed and characterized. Overall, a total of 798 different chemical reactions were recorded and some new chemical reaction pathways were discovered. We believe that the present work heralds the dawn of a new era in which neural network-based reactive MD simulation can be practically applied to simulating important complex reaction systems at ab initio level, which provides atomic-level understanding of chemical reaction processes as well as discovery of new reaction pathways at an unprecedented level of detail beyond what laboratory experiments could accomplish.

Gaining insights into combustion processes is challenging due to the complex reactions involved. The present work proposes a neural network potential model trained to ab initio data that enables to simulate the combustion of methane by predicting reactants, products and reaction intermediates.

High-Fidelity Potential Energy Surfaces for Gas-Phase and Gas-Surface Scattering Processes from Machine Learning

In this Perspective, we review recent advances in constructing high-fidelity potential energy surfaces (PESs) from discrete ab initio points, using machine learning tools. Such PESs, albeit with substantial initial investments, provide significantly higher efficiency than direct dynamics methods and/or high accuracy at a level that is not affordable by on-the-fly approaches. These PESs not only are a necessity for quantum dynamical studies because of delocalization of wave packets but also enable the study of low-probability and long-time events in (quasi-)classical treatments. Our focus here is on inelastic and reactive scattering processes, which are more challenging than bound systems because of the involvement of continua. Relevant applications and developments for dynamical processes in both the gas phase and at gas-surface interfaces are discussed.

Energy landscapes for machine learning

Machine learning techniques are being increasingly used as flexible non-linear fitting and prediction tools in the physical sciences. Fitting functions that exhibit multiple solutions as local minima can be analysed in terms of the corresponding machine learning landscape. Methods to explore and visualise molecular potential energy landscapes can be applied to these machine learning landscapes to gain new insight into the solution space involved in training and the nature of the corresponding predictions. In particular, we can define quantities analogous to molecular structure, thermodynamics, and kinetics, and relate these emergent properties to the structure of the underlying landscape. This Perspective aims to describe these analogies with examples from recent applications, and suggest avenues for new interdisciplinary research.

Ab initio potential energy surface and microwave spectra for the H2-HCCCN complex

We present a four-dimensional ab initio potential energy surface of the H₂-HCCCN complex at the coupled-cluster singles and doubles with noniterative inclusion of connected triples [CCSD(T)]-F12 level with a large basis set including an additional set of bond functions. The artificial neural networks method was extended to fit the intermolecular potential energy surface. The complex has a planar linear global minimum with the well depth of 199.366 cm^-1 located at R = 5.09 Å, φ = 0°, θ₁ = 0°, and θ₂ = 180°. An additional planar local minimum is also found with a depth of 175.579 cm^-1 that is located at R = 3.37 Å, φ = 0°, θ₁ = 110°, and θ₂ = 104°. The radial discrete variable representation/angular finite basis representation and the Lanczos algorithm were employed to calculate the rovibrational energy levels for four species of H₂-HCCCN (pH₂-HCCCN, oH₂-HCCCN, pD₂-HCCCN, and oD₂-HCCCN). The rotational frequencies and spectroscopic parameters were also determined for four complexes, which agree well with the experimental values.

Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network

Neural networks are being used to make new types of empirical chemical models as inexpensive as force fields, but with accuracy similar to the ab initio methods used to build them. In this work, we present a neural network that predicts the energies of molecules as a sum of intrinsic bond energies. The network learns the total energies of the popular GDB9 database to a competitive MAE of 0.94 kcal/mol on molecules outside of its training set, is naturally linearly scaling, and applicable to molecules consisting of thousands of bonds. More importantly, it gives chemical insight into the relative strengths of bonds as a function of their molecular environment, despite only being trained on total energy information. We show that the network makes predictions of relative bond strengths in good agreement with measured trends and human predictions. A Bonds-in-Molecules Neural Network (BIM-NN) learns heuristic relative bond strengths like expert synthetic chemists, and compares well with ab initio bond order measures such as NBO analysis.

The many-body expansion combined with neural networks

Fragmentation methods such as the many-body expansion (MBE) are a common strategy to model large systems by partitioning energies into a hierarchy of decreasingly significant contributions. The number of calculations required for chemical accuracy is still prohibitively expensive for the ab initio MBE to compete with force field approximations for applications beyond single-point energies. Alongside the MBE, empirical models of ab initio potential energy surfaces have improved, especially non-linear models based on neural networks (NNs) which can reproduce ab initio potential energy surfaces rapidly and accurately. Although they are fast, NNs suffer from their own curse of dimensionality; they must be trained on a representative sample of chemical space. In this paper we examine the synergy of the MBE and NN's and explore their complementarity. The MBE offers a systematic way to treat systems of arbitrary size while reducing the scaling problem of large systems. NN's reduce, by a factor in excess of 10⁶, the computational overhead of the MBE and reproduce the accuracy of ab initio calculations without specialized force fields. We show that for a small molecule extended system like methanol, accuracy can be achieved with drastically different chemical embeddings. To assess this we test a new chemical embedding which can be inverted to predict molecules with desired properties. We also provide our open-source code for the neural network many-body expansion, Tensormol.

A quantum chemical study on ˙Cl-initiated atmospheric degradation of acrylonitrile

Degradation of acrylonitrile (CH₂CHCN) by reaction with atomic chlorine was studied using quantum chemical methods.