Task-Adaptive Physical Reservoir Computing via Tunable Molecular Communication Dynamics

Physical Reservoir Computing (PRC) offers an efficient paradigm for processing temporal data, yet most physical implementations are static, limiting their performance to a narrow range of tasks. In this work, we demonstrate in silico that a canonical Molecular Communication (MC) channel can function as a highly versatile and task-adaptive PRC whose computational properties are reconfigurable. Using a dual-simulation approach -- a computationally efficient deterministic mean-field model and a high-fidelity particle-based stochastic model (Smoldyn) -- we show that tuning the channel's underlying biophysical parameters, such as ligand-receptor kinetics and diffusion dynamics, allows the reservoir to be optimized for distinct classes of computation. We employ Bayesian optimization to efficiently navigate this high-dimensional parameter space, identifying discrete operational regimes. Our results reveal a clear trade-off: parameter sets rich in channel memory excel at chaotic time-series forecasting tasks (e.g., Mackey Glass), while regimes that promote strong receptor nonlinearity are superior for nonlinear data transformation. We further demonstrate that post-processing methods improve the performance of the stochastic reservoir by mitigating intrinsic molecular noise. These findings establish the MC channel not merely as a computational substrate, but as a design blueprint for tunable, bioinspired computing systems, providing a clear optimization framework for future wetware AI implementations.