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Multimaterial heterostructures have led to characteristics surpassing the individual components. Nature controls the architecture and placement of multiple materials through biomineralization of nanoparticles (NPs); however, synthetic heterostructure formation remains limited and generally departs from the elegance of self-assembly. Here, a class of block polymer structure-directing agents (SDAs) are developed containing repeat units capable of persistent (covalent) NP interactions that enable the direct fabrication of nanoscale porous heterostructures, where a single material is localized at the pore surface as a continuous layer. This SDA binding motif (design rule 1) enables sequence-controlled heterostructures, where the composition profile and interfaces correspond to the synthetic addition order. This approach is generalized with 5 material sequences using an SDA with only persistent SDA-NP interactions (“P-NP1−NP2”; NPi = TiO2, Nb2O5, ZrO2). Expanding these polymer SDA design guidelines, it is shown that the combination of both persistent and dynamic (noncovalent) SDA-NP interactions (“PD-NP1−NP2”) improves the production of uniform interconnected porosity (design rule 2). The resulting competitive binding between two segments of the SDA (P- vs D-) requires additional time for the first NP type (NP1) to reach and covalently attach to the SDA (design rule 3). The combination of these three design rules enables the direct self-assembly of heterostructures that localize a single material at the pore surface while preserving continuous porosity. 

The continuous double auction (CDA) is the predominant mechanism in modern securities markets. Many agent-based analyses of CDA environments rely on simple non-adaptive trading strategies like Zero Intelligence (ZI), which (as their name suggests) are quite limited. We examine the viability of this reliance through empirical game-theoretic analysis in a plausible market environment. Specifically, we evaluate the strategic stability of equilibria defined over a small set of ZI traders with respect to strategies found by reinforcement learning (RL) applied over a much larger policy space. RL can indeed find beneficial deviations from equilibria of ZI traders, by conditioning on signals of the likelihood a trade will execute or the favorability of the current bid and ask. Nevertheless, the surplus earned by well-calibrated ZI policies is empirically observed to be nearly as great as what the adaptive strategies can earn, despite their much more expressive policy space. Our findings generally support the use of equilibrated ZI traders in CDA studies.