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In a quasiclassical trajectory simulation, the vibrational modes are initialised with quantised vibrational energies, but vibrational phases are sampled by Monte Carlo. This requires an algorithm to assign coordinates and momenta to the various atoms. In this work, we present two methods for implementing this for nonrotating polyatomic molecules, namely, fixed-energy vibrational-state-selected initial conditions and thermal initial conditions. We also present a method for initiating classical trajectories with a ground-state Wigner distribution. These vibrational treatments are sufficient to initialise trajectories for unimolecular processes, and we also show how they can be applied to simulate bimolecular collision processes. The treatments of unimolecular and bimolecular collision processes are available in two Python codes called wigner_state_selected.py and bimolecular_collision.py, respectively, which will generate initial condition files that are recognisable by the SHARC and SHARC-MN computer programs for dynamics calculations. Both codes are available as standalone programs, as well as being included in SHARC-MN, and they will be included in future versions of SHARC. The methods implemented in these codes are mostly also available in the ANT computer program, and those that are not available in ANT will be incorporated in future versions of ANT. 

Creating analytic representations of multiple potential energy surfaces for modeling electronically nonadiabatic processes is a major challenge being addressed in various ways by the chemical dynamics community. In this work, we introduce a new method that can achieve convenient learning of multiple potential energy surfaces (PESs) and their gradients (negatives of the forces) for a polyatomic system. This new method, called compatibilization by deep neural network (CDNN), is demonstrated to be accurate and, even more importantly, to be automatic. The only required input is a database with geometries and potential energies. The method produces a matrix, called the compatible potential energy matrix (CPEM), that may be interpreted as the electronic Hamiltonian in an implicit nonadiabatic basis, and the analytic adiabatic potential energy surfaces and their gradients are obtained by diagonalization and automatic differentiation. We show that the CPEM, which is neither adiabatic nor necessarily diabatic, can be discovered automatically during the learning procedure by the special design of a CDNN architecture. We believe that the CDNN method will be very useful in practice for learning coupled PESs for polyatomic systems because it is accurate and fully automatic.