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

合作研究：整合物理和生成机器学习模型进行逆向材料设计

• 批准号: 1940166
• 负责人: Yifei Mo
• 金额: $ 40万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1940166&HistoricalAwards=false
• 关键词: Collaborative; Research; Integrating; Physics; Generative

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.

该项目旨在解决数据密集型材料科学和工程中的重大挑战，以找到具有所需特性的更好材料，通常旨在提高特定应用中的性能。该项目旨在解决这一重大挑战，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将联合收割机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，以生成信息丰富的材料数据，该数据与生成机器学习（ML）算法相结合，以有效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。该项目将通过多学科合作进行，涉及物理学，材料科学与工程，计算机科学和数学的研究人员。由此产生的多学科环境促进了下一代精通数据的科学家的培训，他们将参与多学科的合作研究。 现有的金属有机骨架（MOF）和固态电解质材料的计算设计方法主要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方法，该方法集成了第一原理计算，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生成的候选材料的属性。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，用于快速计算扩散率，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）分别使用MOF材料和固体电解质的持久性同源和布拉维晶格序列表示来构建深度卷积神经网络（DCNN）模型，以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所发现的材料。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、新型数据驱动的材料设计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。教育和推广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，该奖项由HDR和NSF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NSF的法定使命，并被认为值得通过以下方式获得支持：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。

项目成果

Frustration in Super‐Ionic Conductors Unraveled by the Density of Atomistic States

原子态密度揭示了超离子导体的挫败感

• DOI: 10.1002/ange.202215544
• 发表时间: 2023
• 期刊: Angewandte Chemie
• 作者: Wang, Shuo;Liu, Yunsheng;Mo, Yifei
• 通讯作者: Mo, Yifei

Can Substitutions Affect the Oxidative Stability of Lithium Argyrodite Solid Electrolytes?

取代会影响锂银矿固体电解质的氧化稳定性吗？

• DOI: 10.1021/acsaem.1c03599
• 发表时间: 2022
• 期刊: ACS Applied Energy Materials
• 影响因子: 6.4
• 作者: Banik, Ananya;Liu, Yunsheng;Ohno, Saneyuki;Rudel, Yannik;Jiménez-Solano, Alberto;Gloskovskii, Andrei;Vargas-Barbosa, Nella M.;Mo, Yifei;Zeier, Wolfgang G.
• 通讯作者: Zeier, Wolfgang G.

Superionic Conducting Halide Frameworks Enabled by Interface-Bonded Halides

• DOI: 10.1021/jacs.2c09446
• 发表时间: 2022-12-30
• 期刊: JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
• 影响因子: 15
• 作者: Fu, Jiamin;Wang, Shuo;Sun, Xueliang
• 通讯作者: Sun, Xueliang