| Mar 26, 2022 |
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(Nanowerk News) The traditional trial-and-error method in material research cannot meet the growing demand of various high-performance materials, and developing effective paradigm of material science is extremely urgent.
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In a study published in National Science Review (“Material research from the viewpoint of functional motifs”), the research group led by Prof. GUO Guocong from Fujian Institute of Research on the Structure of Matter of the Chinese Academy of Sciences proposed a new research paradigm for material studies based on the “functional motif” concept.
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Functional motif was defined as the critical microstructure units (e.g., constituent components and building blocks) that play a decisive role in generating certain material functions. These units could not be replaced by other structure units without losing or significantly suppressing the relevant functions. The functional motif paradigm starts with the main aspects of microscopic structures and the properties materials.
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The properties of materials are determined by their functional motifs and how they are arranged in the materials, with the latter determining the quantitative structure–property relationships. Uncovering the functional motifs and their arrangements is crucial in understanding the properties of materials, and the functional motif exploration enables the rational design of new materials with desired properties.
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Given the importance of microscopic structures in the functional motif paradigm, it is necessary to fully understand material structures. The hierarchy of material structure involves information crossing multiple length and time scales.
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The researchers in this study classified the material structures into macroscopic, mesoscopic, and microscopic structures, and further classified microscopic structures into six types, i.e., the crystal, magnetic, aperiodic, defect, local, and electronic structures. For each type of microscopic structure, they presented the role of functional motifs and their arrangements in determining properties with representative functional materials.
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They took infrared (IR) nonlinear optical (NLO) materials as an example to introduce the function-oriented design strategy of new functional materials, in which the role of functional motifs of materials is stressed in the design of materials. This strategy differs from the traditional structure-oriented design strategy.
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Besides, the researchers unveiled the important role of high-throughput experimentation and calculation in material studies and the challenges for extracting functional motifs from a huge amount of data on material structures and properties.
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Machine learning is expected to be useful for efficiently predicting material properties and screening materials with desired properties. For the design of new materials, developing sufficiently reliable material structures and property databases and new effective methods for extracting functional motifs and structure–property relationships of materials from machine learning models is imperative.
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This study reveals that “functional motif theory” can be useful as a guideline for creating new materials and as a tool for predicting the physicochemical properties of materials.
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