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Designing athermal disordered solids with automatic differentiation
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AI
DOI：10.1038/s43246-024-00583-4.png)
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Abstract
En 中文
The ability to control forces between sub-micron-scale building blocks offers significant potential for designing new materials through self-assembly. Traditionally, this involves identifying a crystal structure with a desired property and then designing building-block interactions so that it assembles spontaneously. However, this paradigm fails for structurally disordered solids, which lack a well-defined structure. Here, we show that disordered solids can still be treated from an inverse self-assembly perspective by bypassing structure and directly targeting material properties. Using the Poisson's ratio as a primary example, we demonstrate how differentiable programming links interaction parameters with emergent behavior, enabling iterative training to achieve the desired Poisson's ratio. We also tune other properties, including pressure and local 8-fold structural order, and can even control multiple properties simultaneously. This robust, transferable, and scalable approach can handle a wide variety of systems and properties, demonstrating the utility of disordered solids as a practical avenue for self-assembly platforms. The bottom-up self-assembly of materials from building blocks for achieving targeted properties is typically best achieved in ordered materials. Here, the inverse self-assembly of disordered materials is demonstrated based on targeting specific material properties, such as Poisson's ratio.
Journal
C
IF:
9.6
Papers:
1.4K
Citations:
4.3K