On the Importance of Microstructure Information in Materials Design: PSP vs PP
On the Importance of Microstructure Information in Materials Design: PSP vs PP
复制标题
DOI：
10.1016/j.actamat.2021.117471
复制
发表时间：
2021-11
期刊：
影响因子：
9.4
通讯作者：
Abhilash Molkeri;Danial Khatamsaz;Richard Couperthwaite;Jaylen James;R. Arróyave;D. Allaire;Ankit Srivastava
中科院分区：
文献类型：
--
作者：
Abhilash Molkeri;Danial Khatamsaz;Richard Couperthwaite;Jaylen James;R. Arróyave;D. Allaire;Ankit Srivastava
目标导向材料设计的重点是找到必要的化学/加工条件以实现所需的性能。在这种情况下，材料的微观结构要么只用于进行多尺度模拟，以建立一个可逆的定量过程-结构-性能（PSP）的关系，或合理化后验潜在的微观结构特征负责实现的性能。然而，材料设计过程本身往往是微观结构不可知的：微观结构仅介导工艺-性能（PP）连接，并且很少用于优化本身（除了一些例外，如建筑材料）。虽然PSP关系的存在是材料科学的中心范式，但对于材料设计来说，似乎只需要关注PP关系。在这项工作中，我们试图解决的问题是否“PSP”是一个上级的材料设计范例的情况下，微观结构本身不能（直接）操纵，以优化材料的性能。为此，我们制定了一个新的microstructure-awarecclosed-loop多保真度贝叶斯优化框架的材料设计，严格证明了在设计过程中的微结构信息的重要性。这里考虑的问题涉及找到化学和工艺参数的正确组合，使模型双相钢的目标机械性能最大化。我们的研究结果清楚地表明，明确纳入材料设计框架的微观结构知识显着提高了材料的优化过程。因此，我们证明，在计算环境中，并为一个特定的代表性问题，其中微观结构干预影响感兴趣的属性，即“PSP”是上级“PP”，当涉及到材料设计。
The focus of goal-oriented materials design is to find the necessary chemistry/processing conditions to achieve the desired properties. In this setting, a material’s microstructure is either only used to carry out multiscale simulations to establish an invertible quantitative process-structure-property (PSP) relationship, or to rationalizea posteriorithe underlying microstructural features responsible for the properties achieved. The materials design process itself, however, tends to bemicrostructure-agnostic: the microstructure only mediates the process-property (PP) connection and is—with some exceptions such as architected materials—seldom used for the optimization itself. While the existence of PSP relationships is the central paradigm of materials science, it would seem that for materials design, one only needs to focus on PP relationships. In this work, we attempt to resolve the issue whether ‘PSP’ is a superior paradigm for materials design in cases where the microstructure itself cannot be (directly) manipulated to optimize materials’ properties. To this end, we formulate a novelmicrostructure-awareclosed-loop multi-fidelity Bayesian optimization framework for materials design and rigorously demonstrate the importance of the microstructure information in the design process. The problem considered here involves finding the right combination of chemistry and processing parameters that maximizes a targeted mechanical property of a model dual-phase steel. Our results clearly show that an explicit incorporation of microstructure knowledge in the materials design framework significantly enhances the materials optimization process. We thus prove, in a computational setting, and for a particular representative problem where microstructure intervenes to influence properties of interest, that ‘PSP’ is superior to ‘PP’ when it comes to materials design.