Yang Zhenze, Yu Chi-Hua, Buehler Markus J
Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA.
Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA.
Sci Adv. 2021 Apr 9;7(15). doi: 10.1126/sciadv.abd7416. Print 2021 Apr.
Materials-by-design is a paradigm to develop previously unknown high-performance materials. However, finding materials with superior properties is often computationally or experimentally intractable because of the astronomical number of combinations in design space. Here we report an AI-based approach, implemented in a game theory-based conditional generative adversarial neural network (cGAN), to bridge the gap between a material's microstructure-the design space-and physical performance. Our end-to-end deep learning model predicts physical fields like stress or strain directly from the material microstructure geometry, and reaches an astonishing accuracy not only for predicted field data but also for derivative material property predictions. Furthermore, the proposed approach offers extensibility by predicting complex materials behavior regardless of component shapes, boundary conditions, and geometrical hierarchy, providing perspectives of performing physical modeling and simulations. The method vastly improves the efficiency of evaluating physical properties of hierarchical materials directly from the geometry of its structural makeup.
材料设计是一种开发前所未有的高性能材料的范例。然而，由于设计空间中组合数量庞大，找到具有卓越性能的材料在计算或实验上往往难以处理。在此，我们报告一种基于人工智能的方法，该方法在基于博弈论的条件生成对抗神经网络（cGAN）中实现，以弥合材料微观结构（设计空间）与物理性能之间的差距。我们的端到端深度学习模型直接从材料微观结构几何形状预测应力或应变等物理场，不仅在预测场数据方面，而且在衍生材料性能预测方面都达到了惊人的准确性。此外，所提出的方法通过预测复杂材料行为提供了可扩展性，而无需考虑组件形状、边界条件和几何层次结构，为进行物理建模和模拟提供了思路。该方法极大地提高了直接从分层材料的结构组成几何形状评估其物理性能的效率。
