Department of Advanced Interdisciplinary Studies, The University of Tokyo, 4-6-1 Komaba, Meguro, Tokyo, 153-8904, Japan.
Institute for Industrial Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-0082, Japan.
Sci Rep. 2022 Aug 20;12(1):14238. doi: 10.1038/s41598-022-17614-0.
In materials science, machine learning has been intensively researched and used in various applications. However, it is still far from achieving intelligence comparable to that of human experts in terms of creativity and explainability. In this paper, we investigate whether machine learning can acquire explainable knowledge without directly introducing problem-specific information such as explicit physical mechanisms. In particular, a potential of machine learning to obtain the capability to identify a part of material structures that critically affects a physical property without human prior knowledge is mainly discussed. The guide for constructing the machine learning framework adopted in this paper is to imitate human researchers' process of thinking in the interpretation and development of materials. Our framework was applied to the optimization of structures of artificial dual-phase steels in terms of a fracture property. A comparison of results of the framework with those of numerical simulation based on governing physical laws demonstrated the potential of our framework for the identification of a part of microstructures critically affecting the target property. Consequently, this implies that our framework can implicitly acquire an intuition in a similar way that human researchers empirically attain the general strategy for material design consistent with the physical background.
在材料科学中，机器学习已经得到了广泛的研究和应用。然而，在创造力和可解释性方面，它仍然远远无法达到人类专家的水平。在本文中，我们研究了机器学习是否可以在不直接引入特定于问题的信息（例如明确的物理机制）的情况下获得可解释的知识。特别是，主要讨论了机器学习是否有可能在没有人类先验知识的情况下获得识别对物理性质有重大影响的材料结构部分的能力。本文中采用的机器学习框架的构建指南是模仿人类研究人员在材料解释和开发过程中的思维过程。我们的框架应用于人工双相钢结构的断裂性能优化。框架结果与基于控制物理定律的数值模拟结果的比较表明，该框架具有识别对目标性能有重大影响的微观结构部分的潜力。因此，这意味着我们的框架可以以类似于人类研究人员通过经验获得与物理背景一致的材料设计总体策略的方式，隐式地获得直觉。
