Bridging Global Gaps in AI-Driven Materials Science—Strategies for Inclusive Development

The application of AI in materials science is advancing rapidly, but significant disparities exist between different regions globally. Addressing these disparities is essential for achieving equitable development and leveraging AI's full potential in materials research. This study proposes strategies to ensure inclusive growth in AI-driven materials science:<br/><b>Decentralized Data Sharing</b>: Establish platforms where data providers can continuously benefit from their contributions. This model encourages the sharing of high-quality data, making it accessible to a broader audience and fostering global collaboration and innovation in materials science.<br/><b>Equitable Access to Computational Resources</b>: Advocate for policies and initiatives that distribute computational resources fairly. Breaking the monopolies on computational power ensures that researchers from diverse regions can participate in AI-driven materials research, promoting global equity in scientific advancements.<br/><b>Industry Collaboration and Integration</b>: Foster deep integration between academia and industry to drive practical applications of AI in materials science. Collaborative efforts can bridge the gap between theoretical research and industrial implementation, leading to significant advancements in materials development and sustainability.<br/>We provide a detailed design and analysis of these strategies and present a case study of a digital energy storage project in China. This project illustrates the practical application of our proposed strategies and demonstrates how AI can revolutionize materials research while promoting equitable global development.