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，即找到具有理想性能的更好的材料，通常以提高特定应用的性能为目标。该项目解决了这一重大挑战，重点是寻找用于分离气体混合物的金属有机框架（MOF）材料，并寻找更好的储能电池材料。pi将结合统计力学和凝聚态物理学的理论方法，以及基于物理的模型，生成信息丰富的材料数据，这些数据与生成式机器学习（ML）算法集成，以有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特性。该项目将由多学科合作进行，涉及物理学、材料科学与工程、计算机科学和数学方面的研究人员。由此产生的多学科环境有助于培养下一代精通数据的科学家，他们将从事多学科合作研究。现有的金属有机框架（MOF）和固态电解质材料的计算设计方法主要是基于已知材料的筛选或假设材料的枚举搜索。该项目开发了一种新的方法，该方法集成了第一性原理计算、实验数据和基于物理模型生成的丰富数据，以训练广义对抗网络（GAN）模型，用于有效搜索材料设计空间，并训练深度卷积神经网络（DCNN）模型，用于快速准确地筛选GAN生成的候选材料的性质。此外，基于图形的GAN模型将用于MOF拓扑探索，并可应用于其他纳米材料的设计。更具体地说，研究人员将：1)开发和利用基于物理的模型来快速计算诸如扩散率、离子电导率和机械稳定性等特性；2)开发具有内置物理规则的生成对抗网络（GAN）模型，用于有效探索MOF材料和固体电解质的化学设计空间；3)分别利用MOF材料和固体电解质的持久同源性和Bravais晶格序列表示，构建深度卷积神经网络（Deep Convolutional Neural Network， DCNN）模型，快速准确地预测生成材料的物理性质；4)应用高级量子力学计算来验证发现的材料。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、材料设计的新型数据驱动方案，以及实现先进数据科学技术的理论方法。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。将结合拟议的研究活动拟订和开展教育和外联活动。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HDR和美国国家科学基金会数学和物理科学理事会材料研究部共同支持。该奖项反映了美国国家科学基金会的法定使命，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。

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.