A new research centre at the National University of Singapore (NUS) is bringing together data science, AI and computational methods with deep insights from social sciences and humanities to better understand complex social phenomena and develop solutions to pressing societal challenges. By combining technological innovation with human insight, the Centre for Computational Social Science and Humanities (CSSH) aims to generate research that improves lives, strengthens institutions, preserves cultural knowledge, and shapes more inclusive and resilient societies in Singapore and beyond.

With the global energy system transitioning to renewable energy, high-efficiency energy storage and conversion technologies have become crucial. However, traditional research paradigms for the research and development (R&D) of energy materials such as batteries and electrocatalysts present the limitations in efficiency. This review systematically summarizes the progress of artificial intelligent (AI) in this field, ranging from classical machine learning (ML) to advanced representation methods such as graph neural networks (GNNs) and transformers that enable precise property prediction and structure generation. It also covers generative models for inverse design and large language models (LLMs) for knowledge extraction, along with key domain databases. Current challenges include limited interpretability and the underutilization of emerging AI technologies. Finally, this review discusses future directions such as the applications of multimodal language models, aiming to provide insights for accelerating high-performance energy materials innovation and advancing the global renewable energy transition.