Department of Chemistry, Stanford University, Stanford, CA 94305.
Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305.
Proc Natl Acad Sci U S A. 2024 Feb 20;121(8):e2310238121. doi: 10.1073/pnas.2310238121. Epub 2024 Feb 15.
The adaptive and surprising emergent properties of biological materials self-assembled in far-from-equilibrium environments serve as an inspiration for efforts to design nanomaterials. In particular, controlling the conditions of self-assembly can modulate material properties, but there is no systematic understanding of either how to parameterize external control or how controllable a given material can be. Here, we demonstrate that branched actin networks can be encoded with metamaterial properties by dynamically controlling the applied force under which they grow and that the protocols can be selected using multi-task reinforcement learning. These actin networks have tunable responses over a large dynamic range depending on the chosen external protocol, providing a pathway to encoding "memory" within these structures. Interestingly, we obtain a bound that relates the dissipation rate and the rate of "encoding" that gives insight into the constraints on control-both physical and information theoretical. Taken together, these results emphasize the utility and necessity of nonequilibrium control for designing self-assembled nanostructures.
生物材料在远离平衡的环境中自组装所表现出的适应性和令人惊讶的涌现特性为设计纳米材料提供了灵感。特别是，控制自组装的条件可以调节材料的性能，但我们还没有系统地理解如何参数化外部控制，也不知道给定的材料可以控制到什么程度。在这里，我们证明通过动态控制生长过程中施加的力，分支肌动蛋白网络可以被赋予超材料特性，并且可以使用多任务强化学习来选择方案。这些肌动蛋白网络根据所选的外部方案具有很大的动态范围内的可调响应，为在这些结构中编码“记忆”提供了一种途径。有趣的是，我们得到了一个与耗散率和“编码”率相关的界，该界提供了对控制约束的深入了解——包括物理和信息理论方面的约束。总之，这些结果强调了非平衡控制在设计自组装纳米结构中的实用性和必要性。
