Machine-Assisted Inverse Design of Patchy Particles for Self-Assembly of Archimedean Tilings.

Self-assembly holds great promise for the development of next-generation materials with highly ordered micro- or nanoscale structures. A primary challenge involves the efficient exploration of high-dimensional parameter spaces to achieve user-desired architectures. In this study, we developed an inverse design strategy for the self-assembly of various Archimedean tilings using one-component patchy particles. The cornerstone of our approach resides in the seamless integration of design space decomposition with machine-assisted optimization techniques and specialized simulation evolution pathways, culminating in a stepwise modular protocol for determining critical particle attributes. Specifically, we employed a genetic algorithm-based backward evolution learning protocol followed by a Bayesian-based forward optimization protocol to sequentially determine the patch position and binding strength of the patchy particle. Consequently, the self-assembly of various Archimedean tilings and even more intricate exotic superlattices was successfully realized at a reduced computational cost. Moreover, our strategy showcases a robust capability to explore the design space, offering simplified or enhanced design schemes for target structures. Overall, our work advances inverse design strategies for fabricating intricately structured and high-performance interfacial materials within the scope of patchy particle models.