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ABSTRACT Advances in digital phenotyping have opened the door to continuous, individualised monitoring of mental health, but realising the full potential of these data demands machine learning models that can operate effectively in ‘small‐data’ regimes—where per‐user data are sparse, irregular and noisy. This article explores the feasibility, challenges and opportunities of small‐data machine learning approaches for forecasting individual‐level mental health trajectories. We examine the limitations of traditional clinical tools and population‐level models and argue that fine‐grained time‐series forecasting, powered by models such as tabular prior‐data fitted networks (TabPFN), Gaussian processes, Kalman filters and meta‐learning strategies, offers a path towards personalised, proactive psychiatry. Emphasis is placed on key clinical requirements: real‐time adaptation, uncertainty quantification, feature‐level interpretability and respect for interindividual variability. We discuss implementation barriers including data quality, model transparency and ethical considerations and propose practical pathways for deployment—such as integrated biosensor platforms and just‐in‐time adaptive interventions (JITAIs). We highlight the emerging convergence of small‐data ML, mobile sensing and clinical insight as a transformative force in mental healthcare. With interdisciplinary collaboration and prospective validation, these technologies have the potential to shift psychiatry from reactive symptom management to anticipatory, personalised intervention. 

Abstract Achieving both high redox activity and rapid ion transport is a critical and pervasive challenge in electrochemical energy storage applications. This challenge is significantly magnified when using large‐sized charge carriers, such as the sustainable ammonium ion (NH₄⁺). A self‐assembled MXene/n‐type conjugated polyelectrolyte (CPE) superlattice‐like heterostructure that enables redox‐active, fast, and reversible ammonium storage is reported. The superlattice‐like structure persists as the CPE:MXene ratio increases, accompanied by a linear increase in the interlayer spacing of MXene flakes and a greater overlap of CPEs. Concurrently, the redox activity per unit of CPE unexpectedly intensifies, a phenomenon that can be explained by the enhanced de‐solvation of ammonium due to the increased volume of 3 Å‐sized pores, as indicated by molecular dynamic simulations. At the maximum CPE mass loading (MXene:CPE ratio = 2:1), the heterostructure demonstrates the strongest polymeric redox activity with a high ammonium storage capacity of 126.1 C g⁻¹and a superior rate capability at 10 A g⁻¹. This work unveils an effective strategy for designing tunable superlattice‐like heterostructures to enhance redox activity and achieve rapid charge transfer for ions beyond lithium.