Our group recently published two new papers advancing AI-driven battery materials design. In Nature Computational Science, we introduce an interpretable AI model that reveals the chemical principles governing ionic conductivity in lithium-ion battery electrolytes. An earlier piece Science Advances also led by Dr. Zhilong Wang, we present an integrated AI–simulation–experimental framework for accelerated electrolyte discovery.
These studies move beyond black-box prediction toward mechanistic insight and practical design guidance for next-generation electrolytes. The work was featured in the Cornell Chronicle and other media outlets, e.g., Phys.org.