CAREER: Understanding Electrochemical Metal Extraction in Molten Salts from First Principles

The CAREER project addresses a critical climate change challenge: developing environmentally friendly methods for producing and recycling key metals such as nickel and cobalt, essential for clean energy technologies. As society shifts from fossil fuels to electric energy, increasing battery, electrolyzer, and fuel cell production is vital. This project contributes by investigating clean, electricity-powered metal extraction processes in molten salts by combining atomic-scale computer simulations with machine learning and data science. These computational tools will enable precise modeling of process steps, offering insights beyond experimental capabilities. This includes understanding mineral dissolution in molten salts and how electrolyte composition impacts energy needs for metal extraction, which is essential for effective process design. Besides advancing scientific research methods, clean electrolytic metal extraction processes promise substantial benefits for national health, prosperity, and welfare by advancing clean energy solutions and reducing fossil fuel dependency and, thereby, their detrimental impacts on health and the environment. Unlike conventional mining processes, clean electrolytic processes can be implemented domestically, reducing the reliance on international supply chains and, thus, enhancing national defense and economic stability. Additionally, the project establishes an annual winter school focusing on data science in electrochemical energy, targeting high-school seniors and undergraduates, especially from underrepresented minorities. This initiative advances education at the intersection of data/computational science and chemical engineering and raises awareness about the global impact of critical materials and sustainable energy practices, contributing to a diverse STEM pipeline.This project adopts a novel approach to studying high-temperature mineral electrolysis in molten salts, a crucial yet under-researched class of processes for clean metal extraction for metal production and recovery from electronics waste. It aims to understand key steps in molten-salt electrolysis through tailored modeling approaches: Electronic density-functional theory for atomic/electronic-scale properties that control redox potentials, first-principles surface-phase diagrams for surface/interfacial effects relevant for dissolution, and both ab initio molecular dynamics (MD) simulations and MD simulations based on machine-learning interatomic potentials for transport properties and solvation. An unsupervised learning approach is used to analyze trajectories from large-scale MD simulations, and computational predictions will be validated against experimental data from collaborators. This comprehensive study seeks to develop benchmarked methods and models for designing molten-salt electrolysis processes, with an initial focus on cobalt and nickel minerals relevant to lithium-ion batteries. These advances in computational process design and implementation have broad applications in computational chemical engineering and materials science, marking progress in integrating first principles theory with data science. Moreover, the project has significant broader impacts. It aids the transition to a clean energy economy by providing new degrees of freedom for the rational design of clean metal extraction processes that are needed for the electrification of industry and to overcome supply-chain challenges. Educationally, it integrates data science in chemical engineering and materials science, preparing students for interdisciplinary manufacturing challenges and fostering workforce development in a key yet underserved sector.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.