Machine Intelligence and Nature-Inspired Computing Lab 

The discovery and development of new functional materials has long been a catalyst for technological advancements, greatly influencing our daily life. However, traditional materials development approach takes 10 to 20 years to bring new functional materials from the laboratory to the marketplace. To expedite this process, the Materials Genome Initiative (MGI) was launched in 2011. The primary goal of MGI is to accelerate the various stages involved in materials development by integrating experimental data, theoretical data and knowledge, and computational models.

Following this approach, our interdisciplinary research team aims to discover high-performance functional materials that can effectively address pressing societal and economical challenges, such as the synthesis of solar fuels, carbon-dioxide removal, long-term energy storage, and renewable fertilizer production. To achieve this goal, we build advanced AI models, including prediction models, interpretable models, and generative models, which leverage established and in-house materials databases. These advanced models play a crucial role in accelerating the screening of target materials, facilitating the generation of novel materials, and providing interpretability of substructures to aid in the synthesis of stable materials. To comprehensively evaluate the potential of discovered high-performance materials and ultimately deploy them in practical applications, we are actively collaborating with academic and industrial partners specializing in catalyst synthesis and characterization, as well as device fabrications.