Topic: Artificial Intelligence for Materials Discovery and Sustainable Energy

Artificial intelligence (AI) is rapidly transforming the paradigm of materials research by enabling data-driven discovery, inverse design, autonomous experimentation, and accelerated optimization of functional materials. Recent advances in machine learning, large language models, generative AI, and autonomous laboratories have significantly expanded the capability of AI to integrate scientific knowledge, predict material properties, design novel materials, and guide experimental synthesis with unprecedented efficiency.

Meanwhile, the global pursuit of sustainable development and carbon neutrality has created an urgent demand for next-generation materials and engineering technologies in energy storage, catalysis, hydrogen production, carbon capture and utilization, membranes, renewable energy conversion, and circular economy. AI-assisted materials discovery is emerging as a key enabler for addressing these grand challenges by shortening development cycles, reducing experimental costs, and accelerating innovation from fundamental research to engineering applications.

This Special Topic aims to provide an interdisciplinary platform for researchers from materials science, chemical engineering, energy engineering, computer science, and related fields to present the latest advances in AI-driven materials discovery for sustainable energy and engineering. We welcome original Research Articles, Reviews, Perspectives, and News & Views covering, but not limited to, the following topics:

● Machine learning for materials discovery and design;

● Large language models and generative AI for materials research;

● Autonomous laboratories and self-driving experimentation;

● Materials informatics and scientific foundation models;

● High-throughput computation and inverse materials design;

● AI for energy materials, catalysis, batteries, and hydrogen technologies;

● Carbon capture, utilization and storage (CCUS) materials;

● Membranes and separation materials;

● Data infrastructures, benchmark datasets, and knowledge engineering for materials innovation.

By bringing together advances in artificial intelligence, materials science, and sustainable energy, this Special Topic seeks to foster interdisciplinary collaboration and accelerate the development of intelligent materials innovation for a sustainable future.

Artificial intelligence, materials discovery, materials informatics, machine learning, large language models, autonomous discovery, sustainable energy, energy materials, catalysis, digital materials design