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

Career项目解决了气候变化的一个关键挑战：开发环境友好型方法来生产和回收镍和钴等关键金属，这些金属对清洁能源技术至关重要。随着社会从化石燃料转向电能，增加电池、电解槽和燃料电池的生产至关重要。该项目通过将原子尺度的计算机模拟与机器学习和数据科学相结合，研究熔盐中清洁的、电力驱动的金属提取过程。这些计算工具将实现工艺步骤的精确建模，提供超出实验能力的洞察力。这包括了解矿物在熔融盐中的溶解，以及电解液成分如何影响金属提取的能量需求，这对于有效的工艺设计是至关重要的。除了改进科学研究方法外，清洁的电解金属提取工艺通过推进清洁能源解决方案和减少对化石燃料的依赖，有望为国家健康、繁荣和福利带来实质性好处，从而减少对健康和环境的不利影响。与传统的采矿工艺不同，清洁的电解工艺可以在国内实施，减少对国际供应链的依赖，从而加强国防和经济稳定。此外，该项目还建立了一年一度的冬季学校，重点是电化学能源方面的数据科学，面向高中生和本科生，特别是来自代表不足的少数族裔的学生。这一倡议促进了数据/计算科学和化学工程交叉领域的教育，提高了人们对关键材料和可持续能源实践的全球影响的认识，促进了STEM管道的多样化。该项目采用了一种新的方法来研究熔盐中的高温矿物电解，这是一类关键但研究不足的工艺，用于清洁金属提取，用于金属生产和从电子废物中回收。它旨在通过量身定制的建模方法来理解熔盐电解中的关键步骤：控制氧化还原势的原子/电子尺度性质的电子密度泛函理论，与溶解相关的表面/界面效应的第一原理表面相图，以及用于迁移性质和溶剂化的从头算分子动力学(MD)模拟和基于机器学习的MD模拟。无监督学习方法被用来分析大规模MD模拟的轨迹，计算预测将与合作者的实验数据进行验证。这项全面的研究旨在开发设计熔盐电解工艺的基准方法和模型，最初的重点是与锂离子电池相关的钴和镍矿物。这些在计算过程设计和实现方面的进展在计算化学工程和材料科学中有着广泛的应用，标志着第一性原理理论与数据科学的结合取得了进展。此外，该项目具有重大的更广泛的影响。它为合理设计工业电气化和克服供应链挑战所需的清洁金属提取工艺提供了新的自由度，从而有助于向清洁能源经济的过渡。在教育方面，它将数据科学整合到化学工程和材料科学中，为学生应对跨学科的制造挑战做好准备，并在一个关键但服务不足的部门培养劳动力。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.