US lab prescribes 'medicines' for EV batteries for longer-lasting power

Scientists at Argonne National Laboratory have developed a machine learning model to identify chemical additives that enhance the performance and longevity of high-voltage lithium-ion batteries, specifically LNMO (lithium, nickel, manganese, oxygen) batteries. These batteries offer higher energy capacity and avoid cobalt, a material with supply chain challenges, but operate at nearly 5 volts—exceeding the stability limit of most electrolytes and causing decomposition issues. To mitigate this, electrolyte additives are used to form a stable interface on electrodes, reducing resistance and degradation. Traditionally, finding effective additives is a slow process, but the Argonne team trained their model on a small dataset of 28 additives and accurately predicted the performance of 125 new combinations, significantly accelerating discovery. The key innovation lies in the model’s ability to link the chemical structure of additives to their impact on battery metrics such as resistance and energy capacity, enabling rapid screening of candidates without extensive experimental trials. This approach demonstrates that a well-chosen, small dataset can train