Topic: Machine Learning-assisted Development of Rechargeable Battery Materials

In the era of big data and artificial intelligence, the machine learning approach has attracted significant attention in the materials science community. It effectively complements traditional research paradigms such as trial-and-error experiments, theory, and simulation. The rechargeable battery materials, including those for lithium-ion batteries, lithium-metal batteries, lithium-sulfur batteries, lithium-air batteries, and a wide range of alternatives, exhibit complex nature and multidimensional design spaces, making it challenging to identify robust design strategies for materials development. To this end, machine learning methods are considered versatile tools to accelerate the design of rechargeable battery materials. This Special Issue, guest-edited by Lei Zhang (Nanjing University of Information Science and Technology) and Siqi Shi (Shanghai University), will focus on machine learning-assisted development of rechargeable battery materials. Specific areas to be covered in this Special Issue include, but are not limited to:

● Development and application of machine learning algorithms for rechargeable battery material design;

● Materials modeling coupled with machine learning for battery material optimization;

● High-throughput experiments and autonomous laboratories for rechargeable battery materials and devices;

● Interpretable machine learning for understanding underlying mechanisms;

● Development and application of machine learning potentials for battery materials;

● Database construction for battery materials and devices;

● Application of natural language processing, image recognition, and data-driven methods for battery materials and devices; 

● Development of battery software and infrastructure. 

31 Jul 2024