The ongoing need for materials with difficult-to-combine properties has driven dramatic advancements in the field of bioinspired and biomimetic (nano)structures. These materials blend order and disorder, making their structures difficult to describe and, thus, reproduce. Their practical design involves the approximate replication of geometries found in biological tissues, aiming to achieve desired functionalities using a diverse array of human-made molecular and nanoscale components. Although this approach led to the successful development of numerous high-performance nanocomposites, the rapidly growing demand for better and better materials in energy, water, health and other technologies necessitates an accelerated design process, multidimensional property assessment and, thus, a shift towards quantitative biomimetics. In this Perspective, we approach the design of complex bioinspired materials from the standpoint of interfacial chemistry and physics. Analysing typical examples of biological composites and their successful replicates, we propose a framework based on Taylor series and property differentials that quantifies their interdependence. Five specific cases are considered for limiting their cross-products in Taylor expansions, including discontinuities of differentials at interfaces and multiple scales of organization. We also discuss how the integration of theory, simulations and machine learning is central to the development of quantitative biomimetics. This approach will enable the n-dimensional optimization of contrarian properties by leveraging materials with a high volumetric density of interfaces, graph theoretical description of complex structures and hierarchical multiscale architectures.

对难以结合特性的材料的持续需求推动了仿生和仿生（纳米）结构领域的巨大进步。这些材料混合了秩序和无序，使它们的结构难以描述，因此难以复制。他们的实际设计涉及对生物组织中发现的几何形状的近似复制，旨在使用各种人造分子和纳米级组件实现所需的功能。尽管这种方法导致了许多高性能纳米复合材料的成功开发，但在能源、水、健康和其他技术领域对越来越好的材料的需求迅速增长，这需要加速设计过程、多维性能评估，从而转向定量仿生学。在这个 Perspective 中，我们从界面化学和物理学的角度来处理复杂仿生材料的设计。通过分析生物复合材料的典型例子及其成功复制，我们提出了一个基于泰勒级数和性质差异的框架，以量化它们的相互依赖性。考虑了五种特定情况来限制它们在 Taylor 展开中的交叉积，包括界面处微分的不连续性和多个组织尺度。我们还讨论了理论、模拟和机器学习的整合如何成为定量仿生学发展的核心。这种方法将通过利用具有高体积界面密度的材料、复杂结构的图论描述和分层多尺度架构来实现逆向属性的 n 维优化。