Adversarial Learning Methods for Modeling and Inverse Design of Soft Materials

软材料建模和逆向设计的对抗性学习方法

• 批准号: 2306101
• 负责人: Paul Atzberger
• 金额: $ 24.99万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2306101&HistoricalAwards=false
• 关键词: Adversarial; Learning; Methods; Modeling; Inverse

The properties of the material world emerges from countless interactions between molecules and other microscopic structures. Soft materials are those which exhibit behaviors having a significant dependence on temperature. This includes liquid crystals used in display technology, gels and colloids used by industry in foods and consumer products, and constituents of biological systems. Insights into behaviors and the design of soft materials with specified target properties poses significant challenges given subtleties in how interactions and rearrangements at the microscopic level can vary with temperature, density, and other physical conditions. This calls for the further development of advanced computational methods for modeling, simulation, and optimization for soft materials. This project contributes new data-driven techniques and software tools for soft materials by leveraging and further developing emerging machine learning methods and simulation approaches. This includes adversarial training methods for learning representations of materials leveraging computational properties of competitive games coupled with further development of deep neural network architectures. The approaches will be used to develop tools for modeling and designing soft materials with target properties and for improving the fidelity and efficiency of computational simulations. Open source software also will be developed and released for use by the community. Outreach activities are planned for promoting diversity and engaging under-represented students both at the University of California Santa Barbara and in the local community. This includes working with local area K-12 schools and colleges on programs to engage students on topics in computation, data science, machine learning, and engineering. Educational activities are also planned providing unique opportunities to train the next generation of researchers and students on recent emerging machine learning approaches at the interface of engineering, mathematics, statistics, and data science.The project addresses challenges in developing data-driven approaches for modeling, simulation, and design of soft materials. The properties of soft materials arise from collective microstructure interactions having energies comparable to thermal fluctuations and from effects spanning a wide range of spatial-temporal scales. Given the role of fluctuations, collective entropic effects play a significant role. This presents computational challenges resulting in expensive large-scale forward simulations to characterize and design materials. The project develops new machine learning approaches and software tools for data-driven modeling and simulation of soft materials. This includes approaches for model reduction by identifying coarse degrees of freedom from high-fidelity simulations, methods for learning model parameters and force interactions, and optimization approaches for design of materials with target properties. The project leverages and further develops recent adversarial learning approaches to learn implicit generative models and other representations for improving the efficiency and fidelity of simulations. Methods are also developed for specific applications for data-driven modeling of colloidal systems and polymeric materials with target properties. Software tools also will be developed and released for the approaches to provide general methods for performing simulations, optimization, and analysis of materials.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.

物质世界的特性是由分子和其他微观结构之间无数的相互作用产生的。 软材料是那些表现出对温度具有显着依赖性的行为的材料。 这包括显示技术中使用的液晶、食品和消费品工业中使用的凝胶和胶体以及生物系统的成分。 鉴于微观水平上的相互作用和重排如何随温度、密度和其他物理条件而变化的微妙之处，对具有特定目标特性的软材料的行为和设计的洞察提出了重大挑战。 这就需要进一步开发用于软材料建模、仿真和优化的先进计算方法。 该项目通过利用和进一步开发新兴的机器学习方法和模拟方法，为软材料提供新的数据驱动技术和软件工具。 这包括利用竞技游戏的计算特性以及深度神经网络架构的进一步开发来学习材料表示的对抗性训练方法。 这些方法将用于开发用于建模和设计具有目标特性的软材料以及提高计算模拟的保真度和效率的工具。开源软件也将被开发和发布以供社区使用。计划开展外展活动，以促进多样性并吸引加州大学圣巴巴拉分校和当地社区中代表性不足的学生。这包括与当地 K-12 学校和学院合作开展项目，让学生参与计算、数据科学、机器学习和工程等主题。 还计划开展教育活动，为下一代研究人员和学生培训工程、数学、统计学和数据科学领域最新出现的机器学习方法提供独特的机会。该项目解决了开发用于软材料建模、仿真和设计的数据驱动方法的挑战。 软材料的特性源于具有与热波动相当的能量的集体微观结构相互作用以及跨越广泛的时空尺度的效应。 鉴于波动的作用，集体熵效应发挥着重要作用。 这带来了计算挑战，导致需要昂贵的大规模正向模拟来表征和设计材料。 该项目开发新的机器学习方法和软件工具，用于软材料的数据驱动建模和仿真。 这包括通过从高保真模拟中识别粗略自由度来简化模型的方法、学习模型参数和力相互作用的方法以及具有目标特性的材料设计的优化方法。 该项目利用并进一步开发最新的对抗性学习方法来学习隐式生成模型和其他表示，以提高模拟的效率和保真度。 还针对特定应用开发了方法，用于对具有目标特性的胶体系统和聚合物材料进行数据驱动建模。 还将开发和发布软件工具，以提供执行材料模拟、优化和分析的通用方法。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。