Abstract

Chemistry plays a key role in many astrophysical situations regulating the cooling and the thermal properties of the gas, which are relevant during gravitational collapse, the evolution of discs and the fragmentation process. In order to simplify the usage of chemical networks in large numerical simulations, we present the chemistry package krome, consisting of a python pre-processor which generates a subroutine for the solution of chemical networks which can be embedded in any numerical code. For the solution of the rate equations, we make use of the high-order solver DLSODES, which was shown to be both accurate and efficient for sparse networks, which are typical in astrophysical applications. krome also provides a large set of physical processes connected to chemistry, including photochemistry, cooling, heating, dust treatment and reverse kinetics. The package presented here already contains a network for primordial chemistry, a small metal network appropriate for the modelling of low metallicities environments, a detailed network for the modelling of molecular clouds, a network for planetary atmospheres, as well as a framework for the modelling of the dust grain population. In this paper, we present an extended test suite ranging from one-zone and 1D models to first applications including cosmological simulations with enzo and ramses and 3D collapse simulations with the flash code. The package presented here is publicly available at https://bitbucket.org/krome/krome_stable.