论文标题
一种监督的机器学习方法，用于加速颗粒复合材料的设计：用于导热率
A Supervised Machine Learning Approach for Accelerating the Design of Particulate Composites: Application to Thermal Conductivity
论文作者
论文摘要
提出了一种基于监督的机器学习（ML）计算方法，用于设计具有所需热导率（TC）的多功能复合材料的设计。设计变量是材料微结构的物理描述符，它们将微结构直接连接到材料的属性。基于SOBOL序列生成了足够大且均匀抽样的数据库。使用有效的致密填料算法实现了微观结构，并使用我们先前开发的快速傅立叶变换（FFT）均质化方法获得了TCS。我们优化的ML方法对生成的数据库进行了训练，并建立了结构和属性之间的复杂关系。最后，讨论了训练有素的ML模型在新型的复合材料的反设计中，液态金属（LM）弹性体具有所需的TC。结果表明，替代模型在预测高保真性FFT模拟的微观结构行为方面是准确的，并且根据案例研究，逆设计在查找微观结构参数方面是强大的。
A supervised machine learning (ML) based computational methodology for the design of particulate multifunctional composite materials with desired thermal conductivity (TC) is presented. The design variables are physical descriptors of the material microstructure that directly link microstructure to the material's properties. A sufficiently large and uniformly sampled database was generated based on the Sobol sequence. Microstructures were realized using an efficient dense packing algorithm, and the TCs were obtained using our previously developed Fast Fourier Transform (FFT) homogenization method. Our optimized ML method is trained over the generated database and establishes the complex relationship between the structure and properties. Finally, the application of the trained ML model in the inverse design of a new class of composite materials, liquid metal (LM) elastomer, with desired TC is discussed. The results show that the surrogate model is accurate in predicting the microstructure behavior with respect to high-fidelity FFT simulations, and inverse design is robust in finding microstructure parameters according to case studies.
