Nanoporous materials (NMs) immersed in electrolytes can achieve approximately 1 % deformation at a low operating voltage of about 1 V. The actuation renders them promising artificial muscles. The actuation performance significantly hinges on the structure and size of nanopores and ligaments in NMs. Consequently, designing an optimal configuration is imperative for excellent performance. The actuation mechanism of NMs involves the coupling of multiple fields at various length scales, posing a formidable challenge to conventional simulation and design approaches. To surmount this challenge, we have developed a computational framework capable of conducting concurrent and sequential multiscale calculations. By utilizing artificial neural network (ANN) surrogate models trained on data obtained through the finite element method (FEM), the framework achieves optimized values for both actuation strain and effective Young's modulus within a designated design space. The constitutive model, which establishes the relationship between surface stress and charges in FEM, is derived from the surface eigenstress model and symbolic regression. This involves utilizing data calculated through joint density functional theory. This framework not only ensures the desired properties but also demonstrates its potential for effectively addressing other multiscale optimization problems.

中文翻译：

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通过多尺度计算和机器学习优化纳米多孔金属执行器

浸入电解质中的纳米多孔材料（NM）可以在约 1 V 的低工作电压下实现约 1% 的变形。这种驱动使它们成为有前途的人造肌肉。驱动性能很大程度上取决于纳米材料中纳米孔和韧带的结构和尺寸。因此，设计最佳配置对于获得卓越性能至关重要。 NM的驱动机制涉及不同长度尺度的多个场的耦合，这对传统的仿真和设计方法提出了巨大的挑战。为了克服这一挑战，我们开发了一个能够进行并发和顺序多尺度计算的计算框架。通过利用基于有限元法 (FEM) 获得的数据训练的人工神经网络 (ANN) 代理模型，该框架在指定的设计空间内实现了驱动应变和有效杨氏模量的优化值。本构模型是从表面特征应力模型和符号回归推导出来的，该模型在有限元法中建立了表面应力与电荷之间的关系。这涉及利用通过联合密度泛函理论计算的数据。该框架不仅确保了所需的属性，而且还展示了其有效解决其他多尺度优化问题的潜力。