Data-driven prediction and optimization of osmotic energy conversion performance in multi-nanochannel systems

Osmotic energy conversion harnesses salinity gradients between seawater and freshwater to generate renewable electricity. Vertically aligned nanochannel membranes show promise for this application owing to their exceptional ion transport characteristics, yet the intricate interplay between channel geometry and energy conversion efficiency remains poorly understood, impeding rational membrane design. Here we present a computational framework that combines finite element simulations, machine learning and multi-objective optimization to elucidate how nanochannel length, diameter, pore density and surface charge govern osmotic energy conversion. We systematically sampled the design space to generate a comprehensive dataset and trained a multilayer perceptron model that achieves prediction accuracy exceeding 95 % while accelerating computations by three orders of magnitude compared with the finite element method (FEM). Shapley additive explanations quantified the relative contributions of each parameter. The analysis revealed synergistic effects, including a critical pore density threshold of 2.5 × 10$^7$ pores/cm$^2$. Above this threshold, nanochannel interactions degrade performance. ... mehrMulti-objective genetic algorithms identified 100 Pareto-optimal solutions that define the parameter ranges for maximizing power output and conversion efficiency. Notably, channel length affects power and efficiency in opposing ways, indicating that design priorities must be carefully balanced. This study provides theoretical guidance for the precise design of vertically aligned nanochannel membranes and highlights the potential of artificial-intelligence-driven materials design in advancing clean energy technologies.