Collaborative Research: Integrating Physics and Generative Machine Learning Models for Inverse Materials Design

该项目旨在解决数据密集型材料科学和工程中的一个重大挑战，即寻找具有所需性能的更好的材料，目标通常是提高特定应用的性能。该项目解决了这一重大挑战，特别关注于寻找用于分离气体混合物的金属有机骨架(MOF)材料，以及寻找更好的电池材料来储存能量。PIS将结合统计力学和凝聚态物理的理论方法，以及基于物理的模型，生成信息丰富的材料数据，并与产生式机器学习(ML)算法集成，以高效地搜索复杂的化学设计空间，并训练深度学习模型，以快速筛选材料性能。这个项目将由来自物理、材料科学和工程、计算机科学和数学的研究人员参与的多学科合作进行。由此产生的多学科环境培养了下一代精通数据的科学家，他们将从事协作的多学科研究。现有的金属有机骨架(MOF)和固体电解质材料的计算设计方法主要是基于对已知材料的筛选或对假设材料的列举搜索。该项目开发了一种新的方法，结合第一性原理计算、实验数据和基于物理模型产生的大量数据来训练广义拮抗网络(GAN)模型以高效地搜索材料设计空间，并训练深度卷积神经网络(DCNN)模型以快速准确地筛选GaN产生的候选材料的性质。此外，基于图的GaN模型将用于MOF拓扑探索，并可应用于其他纳米材料设计。更具体地说，研究人员将：1)开发和开发基于物理的模型，用于快速计算扩散系数、离子导电性和机械稳定性等性质；2)开发具有内置物理规则的生成对抗网络(GAN)模型，用于高效探索MOF材料和固体电解质的化学设计空间；3)分别使用MOF材料和固体电解质的持久性同调和Bravais晶格序列表示，建立深度卷积神经网络(DCNN)模型，用于快速准确预测生成材料的物理性质；4)应用高级别量子力学计算来验证已发现的材料。该项目的成果将加速发现用于气体分离和储能的新型纳米结构材料、锂离子电池材料、新的数据驱动材料设计方案以及实现先进数据科学技术的理论方法。这种高度跨学科的合作将为学生提供与各种学科互动的独特机会，并培养具有多学科合作思维的下一代科学家。教育和外展活动将与拟议的研究活动一起开发和开展。该项目是国家科学基金会利用数据革命(HDR)大创意活动的一部分，由HDR和NSF数学和物理科学局内的材料研究部联合支持。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。

This project is aimed to address a grand challenge in data-intensive materials science and engineering to find better materials with desired properties, often with the goal to enhance performance in specific applications. This project addresses this grand challenge with a specific focus on finding metal organic framework (MOF) materials that are used to separate gas mixtures and finding better battery materials for energy storage. The PIs will combine theoretical methods from statistical mechanics and condensed-matter physics, and physics-based models, to generate information-rich materials data which is integrated with generative machine learning (ML) algorithms to search a complex chemical design space efficiently and to train deep learning models for fast screening of materials properties. This project will be carried out by a multidisciplinary collaboration involving researchers from physics, materials science and engineering, computer science, and mathematics. The resulting multidisciplinary environment fosters training the next generation data savvy scientists who will engage in collaborative multidisciplinary research. Existing approaches for computational design of metal organic frameworks (MOF) and solid-state electrolyte materials are largely based on screening of known materials or enumerative search of hypothetical materials. This project develops a new approach that integrates first principles calculations, experimental data and abundant data generated by physics-based models to train generalized antagonistic network (GAN) models for efficient search of the materials design space, and to train deep convolutional neural network (DCNN) models for fast and accurate screening of properties of the GAN-generated candidate materials. Additionally, graph-based GAN models will be used for MOF topology exploration and can be applied to other nanomaterials designs. More specifically, the investigators will: 1) develop and exploit physics-based models for fast calculation of properties such as diffusivity, ion conductivity, and mechanical stability; 2) develop generative adversarial network (GAN) models with built-in physics rules for efficient exploration of the chemical design space for both MOF materials and solid electrolytes; 3) use persistence homology and Bravais lattice sequence representations of MOF materials and solid electrolytes, respectively, to build Deep Convolutional Neural Network (DCNN) models for fast and accurate prediction of the physical properties of generated materials; 4) apply high-level quantum-mechanical calculations for verification of discovered materials. Accomplishments from this project will lead to accelerated discovery of novel nanostructured materials for gas separation and energy storage, materials for lithium-ion batteries, novel data-driven scheme for materials design, and theoretical methods enabling implementation of advanced data science techniques. The highly interdisciplinary collaboration will offer students unique opportunities to interact with a variety of disciplines, and training the next-generation scientists with the mindset for multidiscipline collaborations. Educational and outreach activities will be developed and undertaken in conjunction with the proposed research activities.This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity, and is jointly supported by HDR and the Division of Materials Research within the NSF Directorate of Mathematical and Physical Sciences.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.