  Exploring nonlinear chemical dynamic systems for information processing has
emerged as a frontier in chemical and computational research, seeking to
replicate the brain's neuromorphic and dynamic functionalities. We have
extensively explored the information processing capabilities of a nonlinear
chemical dynamic system through theoretical modeling by integrating a
non-steady-state proton-coupled charge transport system into reservoir
computing (RC) architecture. Our system demonstrated remarkable success in
tasks such as waveform recognition, voice identification and chaos system
prediction. More importantly, through a quantitative study, we revealed the key
role of the alignment between the signal processing frequency of the RC and the
characteristic time of the dynamics of the nonlinear system, which dictates the
efficiency of RC task execution, the reservoir states and the memory capacity
in information processing. The system's information processing frequency range
was further modulated by the characteristic time of the dynamic system,
resulting in an implementation akin to a 'chemically-tuned band-pass filter'
for selective frequency processing. Our study thus elucidates the fundamental
requirements and dynamic underpinnings of the non-steady-state charge transport
dynamic system for RC, laying a foundational groundwork for the application of
dynamic molecular devices for in-materia computing.
