LEAPS-MPS: Deep Learning Phonons using Graph Neural Networks

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NON-TECHNICAL SUMMARY This project, supported by a LEAPS-MPS award, aims to better understand and predict how atoms vibrate in different materials. These vibrations are important because they affect the properties that materials exhibit, especially at high temperatures. However, current materials databases contain little information about these vibrations, which limits the ability to predict how a given material will behave under various conditions. This project aims to create a new computational framework that uses advanced machine learning techniques to model these atomic vibrations. Applying such a computational framework will facilitate the development of a large materials database that includes thermodynamic properties and will thus help to design new materials for various applications, such as certain alloys, known as high-entropy alloys, and materials for managing heat. To achieve the goal, the PI will use a combination of different modeling techniques, including advanced simulations based on quantum mechanics, high-throughput calculations, and machine learning. This multi-disciplinary approach will enable a deeper understanding of atomic vibrations and their effects on material properties. The project will also support the education of undergraduate and master's students in computational materials science at Portland State University. It will help bridge the gap between traditional mechanical & materials engineering and modern data-driven engineering approaches. The PI will collaborate with several programs at Portland State University to engage with and recruit students from underrepresented groups and students from K-12 levels to participate in the project.TECHNICAL SUMMARYAll materials consist of vibrating atoms, even at absolute zero temperature, due to quantum effects. In periodic solids, these vibrations are quantized as phonons, which reflect themselves in finite-temperature characteristics and energy transfer in solids. However, current material databases lack dynamical information on phonons, limiting the prediction of materials at ambient or higher temperatures. This project, supported by a LEAPS-MPS award, conducts computational research and education activities with the aim to develop a hierarchical computational framework that exploits graph neural networks to model atomic vibrations in solids across the periodic table. This will enable a broadened computational design of high-entropy alloys and of thermal storage and management materials. The specific objectives of this project are: 1) developing universal machine learning force fields using graph neural networks for phonon modeling, 2) expanding the phonon database through uncertainty-informed active learning, and 3) gaining a data-driven understanding of atomic vibrations in shaping thermodynamic stability in multi-component alloys and heat transfer in thermal management materials. This overarching goal will be achieved by a multi-disciplinary approach featuring a synergistic integration of multiple modeling techniques, including advanced anharmonic lattice dynamics simulation, high-throughput density functional theory calculations, and machine learning using graph neural networks.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.