Adversarial Learning Methods for Modeling and Inverse Design of Soft Materials

物质世界的性质来自分子和其他微观结构之间的无数相互作用。 软材料是表现出对温度具有显著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。 深入了解具有特定目标特性的软材料的行为和设计带来了重大挑战，因为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。 这就要求进一步发展先进的计算方法，用于软材料的建模、仿真和优化。 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。 这包括对抗性训练方法，用于利用竞争性游戏的计算特性来学习材料的表示，再加上深度神经网络架构的进一步发展。 这些方法将用于开发具有目标特性的软材料建模和设计工具，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，以促进多样性，并使加州圣巴巴拉大学和当地社区的代表性不足的学生参与进来。这包括与当地的K-12学校和学院合作，让学生参与计算，数据科学，机器学习和工程方面的主题。 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，让他们了解工程、数学、统计和数据科学界面上最新出现的机器学习方法。该项目解决了开发软材料建模、模拟和设计的数据驱动方法的挑战。 软材料的性质产生于具有与热波动相当的能量的集体微结构相互作用和跨越宽范围的时空尺度的效应。 考虑到涨落的作用，集体熵效应起着重要作用。 这带来了计算挑战，导致昂贵的大规模正向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度模拟中识别粗略自由度来进行模型简化的方法，用于学习模型参数和力相互作用的方法，以及用于设计具有目标特性的材料的优化方法。 该项目利用并进一步开发了最新的对抗学习方法，以学习隐式生成模型和其他表示，从而提高模拟的效率和保真度。 方法也被开发用于具有目标特性的胶体系统和聚合物材料的数据驱动建模的特定应用。 软件工具也将被开发和发布的方法，以提供执行模拟，优化和材料分析的一般方法。该奖项反映了NSF的法定使命，并已被认为是值得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的支持。

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.