Closed-Loop Discovery of Energy Materials Empowered by Artificial Intelligence Models

Abstract

Energy materials represent the cornerstone of global energy transformation and low-carbon development, and their innovation and performance optimization are essential to resolving bottlenecks in energy conversion and storage. Conventional research and development modes, including empirical, theoretical, and computational methods, are restricted by low efficiency or inherent conflicts between accuracy and system scale, and can hardly satisfy the requirements of modern high-precision, high-efficiency research. The 4th+ paradigm, an extension of the data-driven paradigm (4th paradigm) empowered by advanced artificial intelligence (AI) and data science, takes universal machine learning interatomic potentials (MLIPs), large language models (LLMs), physics-informed neural networks (PINNs), and generative models as its core tools. It delivers atomic-scale modeling accuracy and efficient knowledge extraction capabilities to overcome these limitations, distinguished by its more sophisticated methodologies for generating, processing, and extracting knowledge from data. This Perspective summarizes the advances of this paradigm in energy materials research and clarifies the mechanisms by which MLIPs and LLMs break through traditional constraints. A future research framework is proposed from four aspects: high-quality experimental databases, machine learning models particularly MLIPs, LLMs, and intelligent agents, and full-process closed-loop systems. The development trends are outlined to support the establishment of intelligent research platforms and promote the discovery and industrial applications of high-performance energy materials.