Learning from nature by leveraging integrative biomateriomics modeling toward adaptive and functional materials

Abstract

AbstractBiological systems generate a wealth of materials, and their design principles inspire and inform scientists from a broad range of fields. Nature often adapts hierarchical multilevel material architectures to achieve a set of properties for specific functions, providing templates for difficult tasks of understanding the intricate interplay between structure–property–function relationships. While these materials tend to be complex and feature intricate functional interactions across scales, molecular-based multiscale modeling, machine learning, and artificial intelligence combined with experimental approaches to synthesize and characterize materials have emerged as powerful tools for analysis, prediction, and design. This article examines materiomic graph-based modeling frameworks for assisting researchers to pursue materials-focused studies in a biological context, and provides an overview of methods that can be applied to bottom-up manufacturing, including a historical perspective of bioinspired materials research. Through the advent of novel modeling architectures and diverse systems from nature, there is potential to develop materials with improved properties. Graphical abstract