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.

翻译：我们提出了一种可微分的抽象化学反应网络（CRN）形式化方法，该方法可经过训练以解决多种计算任务。化学反应网络是自然界中最基础的计算载体之一。我们研究了在质量作用动力学下的充分混合单腔室系统，以及由膜分隔的多腔室系统。结果表明，可微分优化结合适当的正则化技术，能够发现实现各类振荡器及其他化学计算设备的非平凡稀疏反应网络。