Benchmarking machine-readable vectors of chemical reactions on computed activation barriers.

In recent years, there has been a surge of interest in predicting computed activation barriers, to enable the acceleration of the automated exploration of reaction networks. Consequently, various predictive approaches have emerged, ranging from graph-based models to methods based on the three-dimensional structure of reactants and products. In tandem, many representations have been developed to predict experimental targets, which may hold promise for barrier prediction as well.

SPAM(a,b): Encoding the Density Information from Guess Hamiltonian in Quantum Machine Learning Representations.

Recently, we introduced a class of molecular representations for kernel-based regression methods─the spectrum of approximated Hamiltonian matrices (SPAM)─that takes advantage of lightweight one-electron Hamiltonians traditionally used as a self-consistent field initial guess. The original SPAM variant is built from occupied-orbital energies (i.e.

Isotope Effects on the Electronic Spectra of Ammonia from Ab Initio Semiclassical Dynamics.

Despite its simplicity, the single-trajectory thawed Gaussian approximation has proven useful for calculating the vibrationally resolved electronic spectra of molecules with weakly anharmonic potential energy surfaces. Here, we show that the thawed Gaussian approximation can capture surprisingly well even more subtle observables, such as the isotope effects in the absorption spectra, and we demonstrate it on the four isotopologues of ammonia (NH, NDH, NDH, and ND). The differences in their computed spectra are due to the differences in the semiclassical trajectories followed by the four isotopologues, and the isotope effects─narrowing of the transition band and reduction of the peak spacing─are accurately described by this semiclassical method.