Theory has established the importance of geometric phase (GP) effects in the adiabatic dynamics of molecular systems with a conical intersection connecting the ground- and excited-state potential energy surfaces, but direct observation of their manifestation in chemical reactions remains a major challenge. Here, we report a high-resolution crossed molecular beams study of the H + HD → H2+ D reaction at a collision energy slightly above the conical intersection. Velocity map ion imaging revealed fast angular oscillations in product quantum state–resolved differential cross sections in the forward scattering direction for H2products at specific rovibrational levels. The experimental results agree with adiabatic quantum dynamical calculations only when the GP effect is included.

Fitting coupled adiabatic potential energy surfaces using coupled diabatic states enables, for accessible systems, nonadiabatic dynamics to be performed with unprecedented accuracy, when compared with on-the-fly dynamics. On-the-fly dynamics has advantages, not the least of which is the ability to compute molecular properties including electric dipole moments, transition dipole moments, and spin− orbit couplings. The availability of these terms extends the range of processes that can be treated with on-the-fly methods. In this work we use the example of fitting electric dipole and transition dipole moments of the 1,2 1 A states of ammonia to show how to bring these advantages to the fit-coupled-surface method using a diabatic representation.

A method for fitting ab initio determined spin− orbit coupling interactions, in the Breit−Pauli approximation, based on quasidiabatic representations using neural network fits is reported. The algorithm generalizes our recently reported neural network approach for representing the dipole interaction. The S 0 , S 1 , and T 1 states of formaldehyde are used as an example. First, the two singlet states S 0 and S 1 are diabatized with a modified Boys Localization diabatization method. Second, the spin−orbit coupling between singlet and triplet states is transformed to the diabatic representation. This removes the discontinuities in the adiabatic representation. The diabatized spin−orbit couplings are then fit with smooth neural network functions. The analytic representation of spin−orbit coupling interactions in a diabatic basis by neural networks will make accurate full-dimensional quantum dynamical treatment of both internal conversion and intersystem crossing possible, which will help us to gain better understanding of both processes.

We propose a machine-learning approach based on Bayesian optimization to build global potential energy surfaces (PES) for reactive molecular systems using feedback from quantum scattering calculations. The method is designed to correct for the uncertainties of quantum chemistry calculations and yield potentials that reproduce accurately the reaction probabilities in a wide range of energies. These surfaces are obtained automatically and do not require manual fitting of the ab initio energies with analytical functions. The PES are built from a small number of ab initio points by an iterative process that incrementally samples the most relevant parts of the configuration space. Using the dynamical results of previous authors as targets, we show that such feedback loops produce accurate global PES with 30 ab initio energies for the three-dimensional H+H 2  H 2 + H reaction and 290 ab inito energies for the six-dimensional OH + H 2  H 2 O+H reaction. These surfaces are obtained from 360 scattering calculations for H 3 and 600 scattering calculations for OH 3 . We also introduce a method that quickly converges to an accurate PES without the a priori knowledge of the dynamical results. By construction, our method illustrates the lowest number of potential energy points (i.e. the minimum information) required for the non-parametric construction of global PES for quantum reactive scattering calculations.