Accelerating functional material innovation: AI and data-driven approach to advanced electronics technologies

The discovery of new functional materials has traditionally relied on time-consuming and costly trial-and-error methods, often taking over 20 years for a material to move from initial discovery to commercial use. Prof. Ming YANG, Assistant Professor of the Department of Applied Physics of The Hong Kong Polytechnic University, is transforming this process through a data-driven, AI-powered approach that significantly increases the speed, accuracy, and efficiency of identifying advanced materials for electronics and energy technologies.

Unlike traditional databases or search engines that passively retrieve past results, AI-driven models actively learn from large datasets, simulate material behaviour, generate hypotheses and optimise experimental parameters. This allows researchers not only to explore existing knowledge but also to predict new materials and uncover hidden patterns.

Prof. YANG’s research leverages high-throughput first-principles calculations—automated, quantum mechanics-based simulations that evaluate materials without needing physical experiments. In a project focused on high-k dielectric materials for next-generation 2D electronics, his research team began with over 140,000 known compounds. By filtering these using key factors like band gap and dielectric constant, they identified about 1,000 promising candidates. Further semi-automated large-scale simulations narrowed the list to around 20 high-performance dielectric materials for 2D semiconductors. This process is estimated to be 4 times faster than conventional methods.

A major innovation in Prof. YANG’s research is the use of physics-informed machine learning, where physical laws are embedded directly into AI models. This enhances accuracy, reduces reliance on large datasets, lowers energy consumption and improves model transparency. His team recently encodes short-range interaction into AI model, in which only local structures are used for the graph representation, making them especially effective for predicting complex material properties such as adsorption and defect behaviour.

Despite the breakthroughs, challenges remain, particularly the need for greater computing power and smarter algorithms to handle vast material datasets. However, with advances in GPUs, parallel computing, and techniques like surrogate modelling and active learning, the pace of discovery continues to accelerate.

By integrating AI, physics and vast material databases, Prof. YANG is reshaping how new materials are discovered. His research supports faster innovation, reduced costs and sustainable development, while positioning Hong Kong as a leading centre for AI-driven materials science.