Collaborative Research: I-AIM: Interpretable Augmented Intelligence for Multiscale Material Discovery

合作研究：I-AIM：用于多尺度材料发现的可解释增强智能

• 批准号: 1940203
• 负责人: WaiChing Sun
• 金额: $ 41.8万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1940203&HistoricalAwards=false
• 关键词: Collaborative; Research; AIM; Interpretable; Augmented

The ability to model, predict, and improve the mechanical performance of engineering materials such as polymers, composites, and alloys can have a significant impact on manufacturing, with important economic and societal benefits. As advanced computational algorithms and data science approaches become available, they can be harnessed to disrupt the current approaches to materials modeling, and allow for the design and discovery of new high-strength, high-performance materials for manufacturing. Bringing together multidisciplinary teams of researchers can maximize the impact of these new tools and techniques. This Harnessing the Data Revolution Institutes for Data-Intensive Research in Science and Engineering (HDR-I-DIRSE) award supports the conceptualization of an Institute to develop novel data science methods, address fundamental scientific questions of Materials Engineering and Manufacturing, and build such multidisciplinary teams. The project will apply novel data science methods to advance the analysis of large sets of structural data of composite materials and alloys from the atomic scale to correlate with and predict mechanical properties. The methods are based on machine learning techniques and uncertainty quantification, and will help uncover underlying structural features in the materials that determine the properties and performance. The methods and results will help accelerate the development of ultra-high strength and lightweight carbon-based composites for aerospace applications, and multi-element superalloys for more durable engine parts, by navigating in the large possible design space and providing faster predictions than experiments and traditional simulation methods. The project will also lead to new methods and computational algorithms that will become publicly available. The investigators will train graduate and undergraduate students from various disciplines with a focus on engaging women and minorities in STEM fields, develop short courses that integrate novel Materials Science and Engineering applications and Data Science methods, and foster vertical integration of interdisciplinary research from undergraduate students to senior scientists.This project aims at building an effective and interpretable learning framework for materials data across scales to solve a major challenge in current data-driven materials design. The combined Materials Science and Data Science approaches will synergistically contribute to the development and use of interpretable and physics-informed data science methodologies to gain new understanding of mechanical properties of polymer composites and alloys, with the potential to be expanded into different property sets and different systems. The PIs will utilize available data efficiently through combination with physical rules and prior knowledge, to develop an interpretable augmented intelligent system to learn principles behind the association of input structures with material properties with uncertainty quantification. The interconnected tasks involve the (1) collection and curation of large amounts of computational and experimental data for polymer/carbon nanotube composites and alloys from open data sources and targeted calculations and experiments, (2) the development of geometric and topological methods incorporating physical principles to generate a better, more sensitive low-dimensional representation of the multidimensional data and characterize the parameter space related to mechanical properties, (3) the development of a Bayesian deep reinforcement learning framework to generate interpretable knowledge graphs that depict the relational knowledge among physical quantities with uncertainty quantification, and (4) the prediction of mechanical properties to reveal design principles to improve materials performance, evaluate and validate the methods, and develop software for dissemination. This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity and is co-funded by the Division of Civil, Mechanical and Manufacturing Innovation.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.

对聚合物、复合材料和合金等工程材料进行建模、预测和改善机械性能的能力可以对制造业产生重大影响，并带来重要的经济和社会效益。随着先进的计算算法和数据科学方法的出现，它们可以被用来破坏当前的材料建模方法，并允许设计和发现用于制造的新的高强度，高性能材料。将多学科研究人员团队聚集在一起可以最大限度地发挥这些新工具和技术的影响。利用数据革命研究所进行科学和工程数据密集型研究（HDR-I-DIRSE）奖支持研究所的概念化，以开发新的数据科学方法，解决材料工程和制造的基本科学问题，并建立这样的多学科团队。该项目将应用新的数据科学方法，从原子尺度推进对复合材料和合金的大量结构数据的分析，以关联和预测机械性能。这些方法基于机器学习技术和不确定性量化，将有助于揭示材料中决定性能和性能的潜在结构特征。这些方法和结果将有助于加速开发用于航空航天应用的超高强度和轻质碳基复合材料，以及用于更耐用发动机部件的多元素高温合金，方法是在尽可能大的设计空间中导航，并提供比实验和传统模拟方法更快的预测。该项目还将导致新的方法和计算算法，将成为公开可用。调查人员将培训来自不同学科的研究生和本科生，重点是让妇女和少数民族参与STEM领域，开发整合新型材料科学和工程应用以及数据科学方法的短期课程，该项目旨在建立一个有效的和可解释的材料数据学习框架，可扩展以解决当前数据驱动的材料设计中的主要挑战。材料科学和数据科学相结合的方法将协同有助于开发和使用可解释和物理信息的数据科学方法，以获得对聚合物复合材料和合金机械性能的新理解，并有可能扩展到不同的属性集和不同的系统。PI将通过与物理规则和先验知识相结合，有效地利用可用数据，开发一个可解释的增强智能系统，以学习输入结构与材料属性之间的关联原理，并进行不确定性量化。这些相互关联的任务涉及（1）从开放数据源和有针对性的计算和实验中收集和管理聚合物/碳纳米管复合材料和合金的大量计算和实验数据，（2）开发结合物理原理的几何和拓扑方法，以产生更好的，多维数据的更灵敏的低维表示并表征与力学性能相关的参数空间，（3）开发贝叶斯深度强化学习框架，以生成可解释的知识图，该知识图描述了具有不确定性量化的物理量之间的关系知识，以及（4）预测机械性能，以揭示设计原理，从而改善材料性能，评估和验证这些方法，并开发软件供传播。该项目是美国国家科学基金会利用数据革命（HDR）大创意活动的一部分，由土木、机械和制造创新部门共同资助。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。

项目成果

Featured Cover

精选封面

• DOI: 10.1002/nme.7087
• 发表时间: 2022
• 期刊: International Journal for Numerical Methods in Engineering
• 影响因子: 2.9
• 作者: Vlassis, Nikolaos N.;Zhao, Puhan;Ma, Ran;Sewell, Tommy;Sun, WaiChing
• 通讯作者: Sun, WaiChing

An offline multi‐scale unsaturated poromechanics model enabled by self‐designed/self‐improved neural networks

• DOI: 10.1002/nag.3196
• 发表时间: 2021-02
• 期刊: International Journal for Numerical and Analytical Methods in Geomechanics
• 影响因子: 4
• 作者: Y. Heider;H. S. Suh;WaiChing Sun

Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening

• DOI: 10.1016/j.cma.2021.113695
• 发表时间: 2021-04
• 期刊: Computer Methods in Applied Mechanics and Engineering
• 影响因子: 7.2
• 作者: Nikolaos N. Vlassis;WaiChing Sun

Molecular dynamics inferred transfer learning models for finite‐strain hyperelasticity of monoclinic crystals: Sobolev training and validations against physical constraints

• DOI: 10.1002/nme.6992
• 发表时间: 2022-04
• 期刊: International Journal for Numerical Methods in Engineering
• 影响因子: 2.9
• 作者: Nikolaos N. Vlassis;Puhan Zhao;R. Ma;Tommy Sewell;WaiChing Sun

Geometric deep learning for computational mechanics Part I: Anisotropic Hyperelasticity

• DOI: 10.1016/j.cma.2020.113299
• 发表时间: 2020-01
• 期刊: ArXiv
• 作者: Nikolaos N. Vlassis;R. Ma;WaiChing Sun