Peking University, June 18, 2025: A collaborative research team led by Professor Pan Feng from the School of New Materials at Peking University Shenzhen Graduate School has developed a topology-based variational autoencoder framework (PGH-VAEs) to enable the interpretable inverse design of catalytic active sites. Their study, titled “Inverse design of catalytic active sites via interpretable topology-based deep generative models” and published in npj Computational Materials, introduces a novel integration of graph-theoretic structural chemistry, algebraic topology, and deep generative models, enabling the rational design of catalysts with targeted adsorption properties from performance objectives.

Peking University, August 26, 2025: Zero-dimensional(0D) metal hilades are highly-valued molecular-level materials that underpin semiconductors used in optoelectronics, particularly for solar cells and light-emitting devices. Though they are well-known for their efficient light emission, general design principles have been lacking, impeding research into their optical properties. A team led by four professors from the College of Chemistry and Molecular Engineering has developed a solvent-embedding method that converts two-dimensional (2D) tin iodide perovskites into 0D structures (Figure 1), offering a versatile synthetic platform for 0D metal halides. The study is published in Nature Chemistry.

Gait assessment plays a critical role in the diagnosis and management of neurological disorders. Although AI-based models offer the potential to enhance the objectivity and accessibility of clinical gait analysis, most existing models are limited by their dependence on specific patient populations and sensor configurations, largely due to the scarcity of diverse clinical datasets. In a recent study, researchers from IBM Research, Cleveland Clinic, and University of Tsukuba addressed this limitation by developing a novel framework that integrates synthetic gait data generated by generative AI with physics-based musculoskeletal simulation. This approach supports the development of scalable and generalizable gait analysis models applicable across heterogeneous patient groups and sensor environments.