Collaborative Research: Multiscale Modeling of Amorphous Solids - Energy Landscapes to Failure Prediction

合作研究：非晶固体的多尺度建模 - 能源景观到故障预测

• 批准号: 1910066
• 负责人: Michael Falk
• 金额: $ 35.08万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-12-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910066&HistoricalAwards=false
• 关键词: Collaborative; Research; Multiscale; Modeling; Amorphous

NONTECHNICAL SUMMARYThis award supports theoretical and computational research, and education to advance understanding of how amorphous materials fail or break under mechanical stress. Amorphous solids are a category of materials that share the distinction that they do not exhibit any crystal structure. Examples include colloidal pastes, foams, gels, silicate glasses, like window glass and Gorilla(R) glass, and many consumer plastics. This research will lay the groundwork for understanding how these materials respond to stresses and eventually fail so as to guide the use of existing materials as well as the development of new materials. The research focuses on metallic glass as an exemplary amorphous solid. Metallic glasses are an extremely promising emerging class of high strength metallic materials, but their application is limited by their failure mechanisms and the current inability to predict their behavior when subjected to mechanical loads. Improved predictive capabilities are necessary for guiding the design and utilization of failure tolerant metallic glass alloys that will have broad application in medicine, defense and consumer goods. This research will build mathematical descriptions of metallic glasses based on simulations performed on the atomic scale. Computational methods to implement these mathematical descriptions will then be developed so as to estimate both the material behavior and the reliability of the predictions that result. The techniques developed will be broadly disseminated through open source computer codes. Additionally, the project will be integrated with educational research and outreach activities of the investigators, which include broad systemic engagement within Baltimore City elementary schools, improvement of introductory computing education, outreach focused on improving participation of women and URM students at both the high-school and undergraduate levels in engineering research, and public lectures at libraries.NONTECHNICAL SUMMARYThis award supports theoretical and computational research, and education to advance understanding of how amorphous materials fail under mechanical stress. The research team aims to develop physics-based multiscale models of failure processes in amorphous solids, with metallic glass taken as an exemplar material. The physics accessible via atomic scale models of glass structure will be analyzed to obtain statistics of the shear transformation zone defects that control plastic deformation. These will be related to statistical mechanics models of effective temperature that characterize the degree of glass disorder so as to build a constitutive model of plastic deformation. The constitutive model will in turn be incorporated into a high-fidelity 3D viscoplastic finite differencing scheme that adapts techniques originally developed for solving the Navier-Stokes equation. A novel machine learning algorithm will guide the parameterization of the constitutive model from atomistic data so as to inform the continuum method and provide uncertainty quantification. The simulation methods will be pushed to increase the scales on which failure can be modeled by developing a statistical representative-volume element approach incorporating adaptive meshing and resulting in true multiscale simulations of failure in amorphous solids. In doing so, this research is aimed to broadly advance fundamental understanding of how the macroscopic mechanical response of amorphous solid materials is informed by their atomic scale structures. The research will result in the development of methods for making direct connections between atomic scale data and theories applicable on the continuum scale. These will include novel machine learning methods and new numerical schemes for failure prediction that incorporate uncertainty quantification. Ultimately, this work will result in an improved understanding of how amorphous microstructure controls failure on the macro scale.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.

非技术摘要该奖项支持理论和计算研究以及教育，以增进对非晶材料在机械应力下如何失效或断裂的理解。无定形固体是一类材料，其共同特点是不表现出任何晶体结构。例子包括胶体糊剂、泡沫、凝胶、硅酸盐玻璃（如窗玻璃和 Gorilla 玻璃）以及许多消费塑料。这项研究将为了解这些材料如何响应应力并最终失效奠定基础，从而指导现有材料的使用以及新材料的开发。该研究的重点是金属玻璃作为非晶态固体的典范。金属玻璃是一类极具前景的新兴高强度金属材料，但其应用受到其失效机制以及目前无法预测其在承受机械载荷时的行为的限制。提高预测能力对于指导容错金属玻璃合金的设计和使用是必要的，这些合金将在医学、国防和消费品领域得到广泛应用。这项研究将基于原子尺度的模拟建立金属玻璃的数学描述。然后将开发实现这些数学描述的计算方法，以估计材料行为和所产生的预测的可靠性。开发的技术将通过开源计算机代码广泛传播。此外，该项目将与研究人员的教育研究和外展活动相结合，其中包括巴尔的摩市小学的广泛系统参与、计算机入门教育的改进、重点关注提高高中和本科阶段女性和 URM 学生参与工程研究的外展活动，以及图书馆的公开讲座。非技术摘要该奖项支持理论和计算研究以及促进理解的教育 非晶材料在机械应力下如何失效。该研究小组的目标是开发基于物理的非晶固体失效过程的多尺度模型，以金属玻璃作为示例材料。将分析通过玻璃结构的原子尺度模型获得的物理学，以获得控制塑性变形的剪切转变区缺陷的统计数据。这些将与表征玻璃无序程度的有效温度统计力学模型相关联，从而建立塑性变形的本构模型。本构模型随后将被纳入高保真 3D 粘塑性有限差分方案中，该方案采用最初为求解纳维-斯托克斯方程而开发的技术。一种新颖的机器学习算法将指导原子数据本构模型的参数化，从而为连续体方法提供信息并提供不确定性量化。通过开发结合自适应网格划分的统计代表性体积单元方法，模拟方法将被推动扩大故障建模的规模，从而对非晶固体中的故障进行真正的多尺度模拟。在此过程中，这项研究旨在广泛推进对非晶固体材料的宏观机械响应如何由其原子尺度结构影响的基本理解。该研究将开发出在原子尺度数据和适用于连续体尺度的理论之间建立直接联系的方法。这些将包括新颖的机器学习方法和包含不确定性量化的故障预测的新数值方案。最终，这项工作将提高对非晶微观结构如何在宏观尺度上控制失效的理解。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Revealing Local Order via High Energy EELS

• DOI: 10.1016/j.mtnano.2022.100298
• 发表时间: 2022-12
• 期刊: Materials Today Nano
• 影响因子: 10.3
• 作者: J. Hart;A. Lang;Yuanyuan Li;S. Shahrezaei;Darius D. Alix-Williams;M. Falk;S. Mathaudhu;A. Frenkel;M. Taheri

Manifold learning for coarse-graining atomistic simulations: Application to amorphous solids

• DOI: 10.1016/j.actamat.2021.117008
• 发表时间: 2021-03
• 期刊: Acta Materialia
• 影响因子: 9.4
• 作者: Katiana Kontolati;Darius D. Alix-Williams;Nicholas M. Boffi;M. Falk;C. Rycroft;M. Shields

Atomic nonaffinity as a predictor of plasticity in amorphous solids

• DOI: 10.1103/physrevmaterials.5.025603
• 发表时间: 2019-05
• 期刊: Physical Review Materials
• 影响因子: 3.4
• 作者: Bin Xu;M. Falk;S. Patinet;P. Guan

Predicting plastic events and quantifying the local yield surface in 3D model glasses

• DOI: 10.1016/j.jmps.2021.104671
• 发表时间: 2021-10
• 期刊: Journal of the Mechanics and Physics of Solids
• 影响因子: 5.3
• 作者: Dihui Ruan;S. Patinet;M. Falk

Predicting failure in disordered solids from structural metrics

从结构指标预测无序固体的失效

• DOI: 10.1103/physrevmaterials.00.003600
• 发表时间: 2020
• 期刊: Physical review materials
• 影响因子: 3.4
• 作者: Richard, D;Ozawa, M;Patinet, S;Stanifer, E;Shang, B;Ridout, S;Zhang, G;Morse, P;Barrat, J-L
• 通讯作者: Barrat, J-L