Liu Shusen, Kailkhura Bhavya, Zhang Jize, Hiszpanski Anna M, Robertson Emily, Loveland Donald, Zhong Xiaoting, Han T Yong-Jin
Center for Applied Scientific Computing, Computation Directorate and Materials Science Division, Physical and Life Science Directorate, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States.
ACS Omega. 2022 Jan 7;7(3):2624-2637. doi: 10.1021/acsomega.1c04796. eCollection 2022 Jan 25.
The materials science community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges. However, despite their effectiveness in building highly predictive models, e.g., predicting material properties from microstructure imaging, due to their opaque nature fundamental challenges exist in extracting meaningful domain knowledge from the deep neural networks. In this work, we propose a technique for interpreting the behavior of deep learning models by injecting domain-specific attributes as tunable "knobs" in the material optimization analysis pipeline. By incorporating the material concepts in a generative modeling framework, we are able to explain what structure-to-property linkages these black-box models have learned, which provides scientists with a tool to leverage the full potential of deep learning for domain discoveries.
材料科学界越来越关注利用深度学习的力量来解决各种领域挑战。然而，尽管深度学习在构建高度预测性模型方面很有效，例如从微观结构成像预测材料性能，但由于其不透明的性质，从深度神经网络中提取有意义的领域知识存在根本性挑战。在这项工作中，我们提出了一种技术，通过在材料优化分析流程中注入特定领域属性作为可调“旋钮”来解释深度学习模型的行为。通过将材料概念纳入生成建模框架，我们能够解释这些黑箱模型学到了哪些结构-性能联系，这为科学家提供了一种工具，以充分利用深度学习在领域发现方面的全部潜力。
