Batteries, like humans, require medicine to function at their best. In battery technology, this medicine comes in the form of electrolyte additives, which enhance performance by forming stable interfaces, lowering resistance and boosting energy capacity.

Finding the right electrolyte additive for a battery is much like prescribing the right medicine. With hundreds of possibilities to consider, identifying the best additive for each battery is a challenge due to the vast number of possibilities and the time-consuming nature of traditional experimental methods.

Researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory are using machine learning models to analyze known electrolyte additives and predict combinations that could improve battery performance. They trained models to forecast key battery metrics, like resistance and energy capacity, and applied these models to suggest new additive combinations for testing.

By combining machine learning with experimental testing, researchers quickly identified effective electrolyte additives, accelerating the discovery process compared with traditional methods, which are costly as well as time-consuming.

Batteries composed of lithium, nickel, manganese and oxygen, known as LNMO, operate at a high voltage and offer significant advantages over traditional batteries. They have higher energy capacity and eliminate the need for cobalt, a critical material associated with supply chain concerns.

While the higher voltage of LNMO batteries offers benefits, it also presents significant challenges. LNMO batteries operating at 5 volts far exceed the stability limit of any known electrolyte.

Introducing an electrolyte additive to the LNMO battery could help limit decomposition and improve battery performance. The researchers found that the ideal additive decomposes during the first few battery cycles, forming a stable interface on both electrode interfaces. This layer helps lower resistance, which means less energy is wasted and less degradation occurs, boosting the battery's energy output.

After training the model, researchers were able to predict the performance of 125 new combinations of additives. The model successfully identified several promising additives that improved battery performance, outperforming additives from the initial data. This method not only saved time and resources but also demonstrated how machine learning can accelerate the discovery of new materials with desired properties for better batteries.

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