Prof. Yang Hao
Queen Mary University London

Professor Yang Hao is QinetiQ/Royal Academy of Engineering Research Chair at Queen Mary University of London. He also serves in the management team of Cambridge Graphene Centre since 2013. Prof. Hao was the Editor-in-Chief for the IEEE Antennas and Wireless Propagation Letters. He founded a new open access journal and is now the Editor-in-Chief of EPJ Applied Metamaterials.

His work has been recognised both nationally and internationally through his books “Antennas and Radio Propagation for Body-Centric Wireless Communications” and “FDTD Modeling of Metamaterials: Theory and Applications,” (Artech House, USA) and highly cited papers published in leading journals, including Nature Communications, Advanced Sceince, Physical Review Letters, Applied Physics Letters, IEEE Proceedings, and Transactions.

His research on transformation optics and metamaterials have led to many tangible benefits for a range of industrial products. One example is metalens antenna designs for satellite communications. This technology has been fully scoped and is currently commercialized under a startup of Isotropic System Limited (All.Space).

Prof. Hao won many accolades, including the prestigious AF Harvey Prize in 2015, the BAE Chairman’s Silver Award in 2014, and the Royal Society Wolfson Research Merit Award in 2013. He was the AdCom Member and currently serves as the Chair of Publication Committee for IEEE Antennas and Propagation Society. Prof. Hao is an elected Fellow of Royal Academy of Engineering, IEEE and IET.

Abstract: Closing the loop for microwave dielectrics research from materials discovery to lab automation

This presentation explores new developments in materials science and electromagnetic device design, leveraging machine learning techniques and lab automation. Firstly, this talk introduces a novel concept, unifying stoichiometry-only and structure-based material descriptors, and showcases a self-attention integrated Graph Neural Network (GNN) that utilizes the formula graph to produce transferable material embeddings. This approach is applied to predict the complex dielectric function of materials, potentially identifying substances with epsilon-near-zero phenomena.

In the realm of perovskites, compositional disorder introduces intriguing complexities, making the discovery of perovskite solid solutions challenging. An unsupervised deep learning strategy is presented, capable of identifying disordered materials' fingerprints based solely on chemical composition. These fingerprints aid in predicting the crystal symmetry of experimental compositions, surpassing several supervised machine learning algorithms. The concept of material analogies is introduced, facilitating the exploration of promising perovskites based on similarity investigations with known materials.

Moreover, the talk introduces an automated materials discovery platform that integrates machine learning-assisted material screening, robotic-controlled synthesis, and high-throughput characterization. This platform drastically reduces the time required for processing tunable perovskites and discovering novel materials.

Finally, the talk will delve into the application of microwave dielectrics for metamaterials and metasurfaces, demonstrating new lens antennas and programmable surfaces for arbitrary amplitude and phase manipulation of electromagnetic waves which lead to promising applications in next-generation wireless networks.

These innovative approaches and techniques presented in this talk promise to revolutionize materials science and electromagnetic device design, offering new insights, accelerated discoveries, and advanced capabilities in various domains.

Prof. Yang HaoQueen Mary University London

Prof. Yang Hao
Queen Mary University London