Adversarial Learning Methods for Modeling and Inverse Design of Soft Materials

物质世界的属性来自于分子和其他微观结构之间的无数相互作用。软材料是那些表现出对温度有很大依赖的行为的材料。这包括用于显示技术的液晶，工业用于食品和消费品的凝胶和胶体，以及生物系统的成分。鉴于微观层面上的相互作用和重排如何随温度、密度和其他物理条件的变化而变化的微妙之处，对具有特定目标属性的软材料的行为和设计的洞察提出了重大挑战。这就要求进一步发展先进的计算方法，用于软材料的建模、模拟和优化。该项目通过利用和进一步开发新的机器学习方法和模拟方法，为软材料贡献了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来学习材料表示的对抗性训练方法，以及进一步开发深度神经网络结构。这些方法将用于开发具有目标属性的软材料的建模和设计工具，并用于提高计算模拟的保真度和效率。开放源码软件也将被开发和发布，供社区使用。计划开展外联活动，促进加州大学圣巴巴拉分校和当地社区中任职人数不足的学生的多样性和参与。这包括与当地的K-12学校和大学合作，让学生在计算、数据科学、机器学习和工程等主题上参与进来。该项目还计划开展教育活动，为下一代研究人员和学生提供独特的机会，培训他们在工程、数学、统计学和数据科学的界面上最近出现的机器学习方法。该项目解决了为软材料的建模、模拟和设计开发数据驱动方法方面的挑战。软材料的性质源于集体微结构相互作用，这些相互作用具有与热涨落相当的能量，并来自跨越广泛时空尺度的效应。考虑到涨落的作用，集体熵效应发挥着重要作用。这带来了计算上的挑战，导致了昂贵的大规模正演模拟来表征和设计材料。该项目开发了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模拟。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，以及具有目标属性的材料设计的优化方法。该项目利用并进一步发展了最近的对抗性学习方法，以学习隐式生成模型和其他表示法，以提高模拟的效率和保真度。还开发了用于胶体系统和具有目标属性的聚合物材料的数据驱动建模的特定应用的方法。还将为这些方法开发和发布软件工具，以提供执行材料模拟、优化和分析的一般方法。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.