Exploring non-steady-state charge transport dynamics in information processing: insights from reservoir computing

Abstract

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. In this study, we have extensively explored the information processing capabilities of a nonlinear chemical dynamic system through theoretical simulation 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 that the alignment between the signal processing frequency of the RC and the characteristic time of the dynamics of the nonlinear system plays a crucial role in this physical reservoir’s performance, directly influencing the efficiency in the task execution, the reservoir states and the memory capacity. The 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 dynamical molecular scale devices for in-materia neuromorphic computing.