Machine learning driven high-throughput discovery of battery electrodes and electrolytes

Abstract

As global energy demand rises, the shift from fossil fuels to renewables like solar, wind, and hydro has become essential.[1] However, the intermittent nature of these sources poses challenges for consistent energy supply.[2] Rechargeable batteries play a vital role in addressing this gap, enabling energy storage and controlled release.[3] Lithium-ion batteries (LIBs) currently dominate due to their high energy density and long cycle life, but concerns over lithium scarcity, safety, cost, and environmental impact are driving the search for alternative battery chemistries.[4, 5] Potassium-ion (K-ion), sodium-ion (Na-ion), magnesium-ion (Mg-ion), calcium-ion (Ca-ion), and aluminum-ion (Al-ion) batteries have attracted significant attention as potential successors to LIBs, owing to the earth-abundance and favourable electrochemical properties of these elements.[6] Nevertheless, the successful deployment of such systems hinges on the rational design of battery components such as electrodes, electrolytes, and interfaces that are both chemically compatible and electrochemically stable. In this regard, a fundamental scientific bottleneck persists: the discovery and optimization of materials for diverse battery chemistries remain heavily reliant on resource-intensive trial-and-error experimental protocols and high-cost quantum mechanical simulations.[7]