Global diabatic potential energy surfaces for the BeH₂⁺ system and dynamics studies on the Be⁺(²P) + H₂(X¹Σ_g⁺) → BeH⁺(X¹Σ⁺) + H(²S) reaction

Abstract

The first global diabatic potential energy surfaces for the Be+(2P) + H2(X1Σg+) → BeH+(X1Σ+) + H(2S) reaction are constructed.

Representing globally accurate reactive potential energy surfaces with complex topography by combining Gaussian process regression and neural networks

“…In the new method, the final analytic PES is fitted by the PIP-NN scheme, which can yield extremely accurate PESs, such as the recently reported ultracold reactive system of KRb + KRb → K 2 + Rb 2 , 65 which presented an RMSE of only 1.86 cm −1 . The Be + ( 2 S) + H 2 reactive PES features multiple wells, barriers, and cusps formed by the avoided crossing effect of the first excited state, 66 which go against the fitting accuracy, and too small RMSE can also increase the risk of long-range potential for the tested system. Although the fitting RMSE does not reach the order of spectroscopic accuracy, the accuracy of the obtained PES is sufficient for dynamics studies on the endothermic reaction of Be + ( 2 S) + H 2 .…”

Nonadiabatic dynamics studies of the H(2S) + RbH(X1Σ+) reaction: based on new diabatic potential energy surfaces

“…The diabatization scheme has been widely used in previous theoretical studies. [4][5][6] Herein, a brief introduction is given, and more details can be found in previous literature. [21][22][23] In C s symmetry, the 1 2 A 0 and 2 2 A 0 states in the RbH 2 system are correlated.…”

“…Thirdly, the integration path through NACME is not unique for systems containing more than two atoms. As a result, some approximation methods have been developed to build diabatic PESs, such as the CI coefficients, 1–3 molecular properties, 4–6 and diabatization by Ansatz 7 methods.…”

Globally Accurate Gaussian Process Potential Energy Surface and Quantum Dynamics Studies on the Li(2S) + Na2 → LiNa + Na Reaction at Low Collision Energies

“…The main challenge in constructing multi-dimensional PESs is to represent the function between the potential energies and the molecular nuclear coordinates based on the discrete ab initio data. Fitting PESs with machine learning models has been gaining popularity in recent years, and using an artificial neural network (NN) [ 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ] or a Gaussian process (GP) [ 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 ] are the two most common approaches. GP is a kernel-based supervised statistical learning method [ 71 ], which has been widely used to solve physical chemistry problems such as mapping high-dimensional PESs and simulating quantum scattering dynamics.…”