  Reservoir computing (RC) is a promising solution for achieving low power
consumption neuromorphic computing, although the large volume of the physical
reservoirs reported to date has been a serious drawback in their practical
application. Here, we report the development of a few-molecule RC that employs
the molecular vibration dynamics in the para-mercaptobenzoic acid (pMBA)
detected by surface enhanced Raman scattering (SERS) with tungsten oxide
nanorod/silver nanoparticles (WOx@Ag-NPs). The Raman signals of the pMBA
molecules, adsorbed at the SERS active site of WOx@Ag-NPs, were reversibly
perturbated by the application of voltage-induced local pH changes in the
vicinity of the molecules, and then used to perform RC of pattern recognition
and prediction tasks. In spite of the small number of molecules employed, our
system achieved good performance, including 95.1% to 97.7% accuracy in various
nonlinear waveform transformations and 94.3% accuracy in solving a second-order
nonlinear dynamic equation task. Our work provides a new concept of molecular
computing with practical computation capabilities.
