Recurrent neural chemical reaction networks that approximate arbitrary dynamics (opens in new tab)

A class of in silico chemical systems is presented whose modular structure is based on artificial neural networks. It is shown that these chemical systems can be trained to approximate arbitrary dynamical behaviors.