Towards Machine Learning Control of Chemical Computers

The behaviour of pulses of Belousov-Zhabotinski (BZ) reaction-diffusion waves can be controlled automatically through machine learning. By extension, a form of chemical network computing, i.e., a massively parallel non-linear computer, can be realised by such an approach. In this initial study a light-sensitive sub-excitable BZ reaction in which a checkerboard image comprising of varying light intensity cells is projected onto the surface of a thin silica gel impregnated with tris(bipyridyl) ruthenium (II) catalyst and indicator is used to make the network. As a catalyst BZ solution is swept past the gel, pulses of wave fragments are injected into the checkerboard grid resulting in rich spatio-temporal behaviour. This behaviour is shown experimentally to be repeatable under the same light projections. A machine learning approach, a learning classifier system, is then shown able to direct the fragments to an arbitrary position through dynamic control of the light intensity within each cell in both simulated and real chemical systems.