具有最优刚度-韧性权衡的微结构复合材料的计算发现
Computational Discovery of Microstructured Composites with Optimal Stiffness-Toughness Trade-Offs
AI总结：
本研究提出一种集成物理实验、数值模拟和人工神经网络的通用流水线，通过嵌套循环提议-验证工作流弥合模拟与现实的差距，以高样本效率发现具有最优刚度-韧性权衡的微结构复合材料，并自动识别增韧机制。
摘要：
刚度与韧性之间的冲突是工程材料设计中的一个基本问题。然而，由于模拟与现实之间的差异以及缺乏对整个Pareto前沿的数据高效探索，系统性地发现具有最优刚度-韧性权衡的微结构复合材料从未得到证明。我们引入了一个通用化的流水线，集成物理实验、数值模拟和人工神经网络来解决这两个挑战。在没有任何预设的材料设计专家知识的情况下，我们的方法实施了一个嵌套循环提议-验证工作流，以弥合模拟到现实的差距，并以高样本效率发现既刚硬又坚韧的微结构复合材料。对Pareto最优设计的进一步分析使我们能够自动识别现有的增韧机制，这些机制先前是通过试错或仿生学发现的。在更广泛的尺度上，我们的方法为固体力学之外各个研究领域的计算设计提供了蓝图，例如聚合物化学、流体动力学、气象学和机器人学。
英文摘要：
The conflict between stiffness and toughness is a fundamental problem in engineering materials design. However, the systematic discovery of microstructured composites with optimal stiffness-toughness trade-offs has never been demonstrated, hindered by the discrepancies between simulation and reality and the lack of data-efficient exploration of the entire Pareto front. We introduce a generalizable pipeline that integrates physical experiments, numerical simulations, and artificial neural networks to address both challenges. Without any prescribed expert knowledge of material design, our approach implements a nested-loop proposal-validation workflow to bridge the simulation-to-reality gap and discover microstructured composites that are stiff and tough with high sample efficiency. Further analysis of Pareto-optimal designs allows us to automatically identify existing toughness enhancement mechanisms, which were previously discovered through trial-and-error or biomimicry. On a broader scale, our method provides a blueprint for computational design in various research areas beyond solid mechanics, such as polymer chemistry, fluid dynamics, meteorology, and robotics.
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。
