Artificial intelligence-enabled optimization of battery-grade lithium carbonate production†

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-10-14 DOI:10.1039/D4DD00159A
S. Shayan Mousavi Masouleh, Corey A. Sanz, Ryan P. Jansonius, Samuel Shi, Maria J. Gendron Romero, Jason E. Hein and Jason Hattrick-Simpers
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Abstract

By 2035, the need for battery-grade lithium is expected to quadruple. About half of this lithium is currently sourced from brines and must be converted from lithium chloride into lithium carbonate (Li2CO3) through a process called softening. Conventional softening methods using sodium or potassium salts contribute to carbon emissions during reagent mining and battery manufacturing, exacerbating global warming. This study introduces an alternative approach using carbon dioxide (CO2(g)) as the carbonating reagent in the lithium softening process, offering a carbon capture solution. We employed an active learning-driven high-throughput method to rapidly capture CO2(g) and convert it to lithium carbonate. The model was simplified by focusing on the elemental concentrations of C, Li, and N for practical measurement and tracking, avoiding the complexities of ion speciation equilibria. This approach led to an optimized lithium carbonate process that capitalizes on CO2(g) capture and improves the battery metal supply chain's carbon efficiency.

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人工智能优化电池级碳酸锂的生产†。
到 2035 年,对电池级锂的需求预计将翻两番。目前,约有一半的锂来自卤水,必须通过软化过程将氯化锂转化为碳酸锂(Li2CO3)。使用钠盐或钾盐的传统软化方法会在试剂开采和电池制造过程中造成碳排放,加剧全球变暖。本研究介绍了一种替代方法,即在锂软化过程中使用二氧化碳(CO2(g))作为碳化试剂,提供碳捕获解决方案。我们采用了一种主动学习驱动的高通量方法来快速捕获 CO2(g)并将其转化为碳酸锂。为了便于实际测量和跟踪,我们对模型进行了简化,将重点放在 C、Li 和 N 的元素浓度上,避免了复杂的离子标示平衡。这种方法优化了碳酸锂工艺,充分利用了二氧化碳(g)的捕获,提高了电池金属供应链的碳效率。
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Back cover ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials. Sorting polyolefins with near-infrared spectroscopy: identification of optimal data analysis pipelines and machine learning classifiers†‡ High accuracy uncertainty-aware interatomic force modeling with equivariant Bayesian neural networks† Correction: A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing
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