Collaborative Research: EAGER: SSMCDAT2023: Data-driven Predictive Understanding of Oxidation Resistance in High-Entropy Alloy Nanoparticles

NONTECHNICAL SUMMARY This award is made on an EAGER proposal. It supports progress on a project advanced at the SSMCDAT 2023 Datathon held at Lehigh University. This project aims to understand the oxidation behavior of high-entropy alloy nanoparticles, which are a new type of alloys containing multiple elements in roughly equal proportions. Oxidation under industrial conditions negatively impacts the performance of these alloys, limiting their broader use. In this project, the team will employ a data-driven approach, combining experimental and computational datasets with targeted experimental synthesis. The goal is to develop reliable predictive models considering uncertainties in both types of data, to lead to a better understanding of the oxidation behavior of high-entropy alloy nanoparticles at the nanoscale. By gaining fundamental knowledge through this interdisciplinary effort spanning materials science, chemistry, and applied mathematics, the project has the potential to enhance the oxidation resistance of high-entropy alloy nanoparticles. It will also provide essential support for critical experimental studies to validate the data-driven models. This research opens new possibilities for innovative strategies to synthesize high-entropy materials, paving the way for exciting advances in future research and technological applications. The project provides comprehensive research training in materials chemistry and data science to graduate students within a collaborative and interdisciplinary research environment. The project will participate in a long program at the Institute of Mathematical and Statistical Innovation and organize a workshop centered around "Uncertainty Quantification for Chemistry and Materials Science". Leveraging the outcomes of the project, the team aims to propel and invigorate data-intensive research, particularly by integrating uncertainty quantification into predictive modeling within the domain of solid state and materials chemistry. TECHNICAL SUMMARY This award is made on an EAGER proposal. It supports progress on a project advanced at the SSMCDAT 2023 Datathon held at Lehigh University. This project aims to gain a mechanistic understanding of the interplay among elemental segregation, migration, and oxidation resistance of high-entropy alloy nanoparticles by integrating experimental and computational tools with modern data science methods. The goal is to establish data-driven materials design strategies that allow precise control over the oxidation kinetics of high-entropy nanoparticles with composition design. The project will leverage existing experimental and computational datasets on thermodynamic and adsorption energetics, and combine this data with supplementary high-throughput first-principles calculations and hybrid molecular dynamics / Monte Carlo simulations, to develop machine learning models for elemental segregation and migration models in high-entropy alloy nanoparticles under oxidation conditions. A novel Gaussian Process regression model, which inherently includes uncertainty quantification and allows for intuitive interpretation, will be developed to predict oxidation behavior. Furthermore, the project will synthesize high-entropy alloy nanoparticles with specific compositions and characterize their structural and oxidation behavior, comparing the results with model predictions. This this research will provide experimentally validated fundamental knowledge regarding the structure-property relationships of high-entropy alloy nanoparticles under oxidation environments. Additionally, the project will establish a valuable suite of analytical and modeling tools for the field of solid-state and materials chemistry. These tools will enable an integrated approach to accelerate experimental-computational design of high-entropy alloy nanoparticles, facilitating theory-guided synthesis research of multicomponent material nanoparticles across a broader chemical space. 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.