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

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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.