Materials informatics, which leverages data from experiments and theoretical calculations, is gaining attention for its potential to accelerate materials development. Not only can this facilitate the discovery of novel materials, but also analyzing large-scale data helps establish new guidelines for materials exploration and universal principles of materials design. When using theoretical calculation data, such as those from first-principles calculations, it is essential to employ accurate and efficient methods and to automate calculations and analyses for reliable prediction and high-throughput screening of materials. In this review, we present our efforts to develop methods for systematically predicting various properties of inorganic materials using high-throughput first-principles calculations and machine learning, and to apply them to the design and discovery of new materials.

This article highlights multimodal AI and self-driving laboratories as emerging approaches in data-driven research and development, particularly in materials chemistry. As data science becomes essential across disciplines, advances in machine learning and accessible robotics have lowered the barriers between cyber and physical domains. However, human researchers remain central in defining goals, designing strategies, and taking responsibility. Multimodal AI integrates diverse data types - such as spectra, images, compositions, and process conditions—enabling insights beyond single-modality analysis. The authors redefine this framework for materials science and demonstrate its effectiveness across various material systems. Self-driving laboratories, in contrast, employ closed-loop frameworks that update experimental conditions based on real-time data, allowing efficient exploration and exploitation, especially in complex systems with limited prior data. These approaches are not merely extensions of automation but require domain knowledge to design and interpret models. Future progress depends not only on technological advances but also on the development of expertise, collaborative environments, and research cultures that fully leverage data-driven methods.