在超材料设计中探索多能性
Prospecting for Pluripotency in Metamaterial Design
AI总结：
本文提出一种结合卷积神经网络与遗传算法的数据驱动策略，设计出能实现多种目标变形的多能组合超材料，克服了传统优化方法在设计空间粗糙时的局限性。
摘要：
从自组装和蛋白质折叠到组合超材料，材料设计中的一个关键挑战是找到正确的相互作用构建块组合，以产生目标特性。这样的结构极难被发现；不仅因为它们稀有，而且通常设计空间非常粗糙，以至于梯度毫无用处，直接优化也无望。在这里，我们通过引入一种双重策略来设计能够实现多种期望变形的超稀有组合超材料，该策略避免了直接优化的缺点。我们首先将卷积神经网络与遗传算法相结合，以探索具有高性能潜力的超材料设计。在我们的案例中，这些超材料具有大量空间扩展模式；它们是多能的。其次，我们利用这个多能设计库来生成具有多种目标变形的超材料，最后通过策略性地放置缺陷对其进行细化。我们的多能、多形状超材料是不可能通过试错或标准优化来设计的。相反，我们的数据驱动方法是系统性的，非常适合解决材料科学中普遍存在的大而棘手的组合问题。
英文摘要：
From self-assembly and protein folding to combinatorial metamaterials, a key challenge in material design is finding the right combination of interacting building blocks that yield targeted properties. Such structures are fiendishly difficult to find; not only are they rare, but often the design space is so rough that gradients are useless and direct optimization is hopeless. Here, we design ultra rare combinatorial metamaterials capable of multiple desired deformations by introducing a two-fold strategy that avoids the drawbacks of direct optimization. We first combine convolutional neural networks with genetic algorithms to prospect for metamaterial designs with a potential for high performance. In our case, these metamaterials have a high number of spatially extended modes; they are pluripotent. Second, we exploit this library of pluripotent designs to generate metamaterials with multiple target deformations, which we finally refine by strategically placing defects. Our pluripotent, multishape metamaterials would be impossible to design through trial-and-error or standard optimization. Instead, our data-driven approach is systematic and ideally suited to tackling the large and intractable combinatorial problems that are pervasive in material science.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
