A Machine Learning Framework for Bridging the Mechanical Responses of a Material at Multiple Structure Length Scales

承载结构的安全性取决于工程师在设计阶段预测工作条件下的行为的能力。这意味着工程师必须准确地了解这些结构中使用的材料的行为。材料中的损伤和失效始于原子尺度，并通过结构长度尺度传播以表现自身。因此，重要的是能够通过基于实验观察和计算建模的多尺度来预测材料响应。人们为实现这一能力付出了大量努力，但从先进的实验技术和基于物理的数值模拟中获得的大量信息已被证明是难以处理的。该奖项旨在通过引入机器学习来优化从不同和广泛的实验和数值数据集中提取和融合信息和知识，从而改变材料力学领域的当前实践。其目标是与当前的材料行为预测协议相比，显着提高效率和产量，这也将加速材料发现过程。预计这些成果将在广泛的先进材料技术领域为美国工业提供显著的竞争优势，包括与医疗保健，能源和国家安全相关的领域。该奖项也将是一个工具，培养研究生和本科生在材料科学，材料力学，数据和信息科学的交叉。研究成果和开发的工具将通过多个工业合作转移到商业实践中。建议通过开发和部署一个新的贝叶斯机器学习框架来实现上述研究目标，该框架以系统地揭示控制感兴趣的多尺度材料现象的物理学为中心。总体策略涉及建立合适的高保真降阶（即，替代）模型来捕获多相多晶微结构中的弹性和塑性变形的局部化张量。反过来，这些模型将被用来制定一个计算效率高的策略贝叶斯序贯设计的实验，确定最佳的实验提供最高的信息（或知识）增益的潜力。因此，将设计和评估几个高通量的实验测定，以严格检查其可靠地校准复杂的塑性理论中的未知材料参数的价值。基于这些调查的结果，新的高通量协议将被设计和实施，以证明显着的成本和节省时间实现的多尺度表征的力学行为的异质结构材料。具体来说，新协议将使用多晶双相钢样品进行验证。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The safety of structures bearing load depends upon the ability of the engineers to predict the behavior under operating conditions during the design phase. This means that the engineers must know the behavior of materials that are used in these structures accurately. Damage and failure in materials start at the atomic scale and propagate through the structural length scales to manifest itself. Therefore, it is important to be able to predict material response through multiple scales based on experimental observations and computational modeling. Much effort has been put towards achieving this capability but the immense amount of information that is obtained from advanced experimental techniques and physics-based numerical simulations has proven intractable. This award aims to transform the current practices in the field of mechanics of materials by introducing machine learning to optimize the extraction and fusion of information and knowledge from the disparate and expansive experimental and numerical datasets. The goal is to dramatically improve the efficiency and output compared to the current protocols of material behavior prediction, which will also accelerate the materials discovery process. It is expected that theses outcomes will provide significant competitive advantages to the US industry in a broad range of advanced materials technology areas, including those related to healthcare, energy, and national security. The award will also be a vehicle to train graduate and undergraduate students at the intersection of materials science, mechanics of materials, and data and information sciences. The research outcomes and developed tools will be transferred into commercial practice through multiple industrial collaborations.It is proposed to accomplish the research objective described above by developing and deploying a novel Bayesian machine learning framework that is centered on systematically uncovering the physics controlling the multiscale materials phenomena of interest. The overall strategy involves establishing suitable high-fidelity reduced-order (i.e., surrogate) models to capture the localization tensors for elastic and plastic deformations in multiphase polycrystalline microstructures. In turn, these models will be used to formulate a computationally efficient strategy for Bayesian sequential design of experiments that identifies the most optimal experiments offering the highest potential for information (or knowledge) gain. As a result, several high-throughput experimental assays will be designed and evaluated to critically examine their value for reliably calibrating the unknown material parameters in sophisticated plasticity theories. Based on the results of these investigations, novel high-throughput protocols will be designed and implemented to demonstrate the significant cost and time savings achieved in the multiscale characterization of the mechanical behavior of heterogeneous structural materials. Specifically, the new protocols will be validated using samples of polycrystalline dual-phase steels.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.