Functionalization of semiconductor photoelectrodes is actively pursued as an approach to improve the efficiency of photoelectrochemical reactions by modulating the semiconductor's barrier height, but the selection of molecules for functionalization remains largely empirical. We propose a simple but effective design strategy for the organic functionalization of photocathodes for high-efficiency hydrogen generation based on first-principles density functional theory (DFT) calculations. The surface dipole of the functionalized photocathode determines its barrier height, which can be optimized to enhance charge separation at the semiconductor-electrolyte interface. Focusing on p-Si(111) photocathodes functionalized with different mixed aryl/methyl monolayers, we use DFT to systematically investigate the effect of - the presence and distribution of pi bonds, binding atom (the atom in the functional group that bonds with the Si surface), functional group length, and electrophilic substituent groups - on the surface dipole and charge rearrangement at the functionalized surface. We find that the most important factors affecting the surface dipole are the intrinsic molecular dipole moment of the organic moiety, the presence of electrophilic substituent groups, and the binding atom. Using these findings, a three-step design strategy is proposed for the experimental realization of high-performing functionalized p-Si(111) photocathodes by maximizing the surface dipole.

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In autonomous robotic decision-making under uncertainty, the tradeoff between exploitation and exploration of available options must be considered. If secondary information associated with options can be utilized, such decision-making problems can often be formulated as a contextual multi-armed bandits (CMABs). In this study, we apply active inference, which has been actively studied in the field of neuroscience in recent years, as an alternative action selection strategy for CMABs. Unlike conventional action selection strategies, it is possible to rigorously evaluate the uncertainty of each option when calculating the expected free energy (EFE) associated with the decision agent's probabilistic model, as derived from the free-energy principle. We specifically address the case where a categorical observation likelihood function is used, such that EFE values are analytically intractable. We introduce new approximation methods for computing the EFE based on variational and Laplace approximations. Extensive simulation study results demonstrate that, compared to other strategies, active inference generally requires far fewer iterations to identify optimal options and generally achieves superior cumulative regret, for relatively low extra computational cost.