  We present a differentiable formulation of abstract chemical reaction
networks (CRNs) that can be trained to solve a variety of computational tasks.
Chemical reaction networks are one of the most fundamental computational
substrates used by nature. We study well-mixed single-chamber systems, as well
as systems with multiple chambers separated by membranes, under mass-action
kinetics. We demonstrate that differentiable optimisation, combined with proper
regularisation, can discover non-trivial sparse reaction networks that can
implement various sorts of oscillators and other chemical computing devices.
