Haidong Wang — Page 2

arXiv2020

Phonon Magic Angle in Two-Dimensional Puckered Homostructures

The emergence of twistronics provides an unprecedented platform to modulate the band structure, resulting in exotic electronic phenomena ranging from ferromagnetism to superconductivity. However, such concept on phonon engineering is still lacking. Here, we extend the 'twistnonics' to 2D puckered materials with a 'phonon magic angle' discovered by molecular dynamics simulation. The phonon magic angle, with the TP-1 and TP-2 direction overlapped, remains a high level or even enhances phonon transport capability due to van der Waals confinement. This novel phenomenon originates from the confined vdW interaction and ordered atomic vibration caused by the perfect lattice arrangement that the atoms of the top layer can be stuck to the spaces of the bottom layer. Moreover, it is found that both the in-plane and out-of-plane thermal transport properties can be effectively regulated by applying the twist. Through the phononic and electronic analysis, the deterioration of phonon transport capability for other twist angles are attributed to the suppression of acoustic phonon modes, reduction of phonon lifetimes and mismatched lattice vibration between layers. Our findings shed light on the twistnonics of low-dimensional asymmetrical materials and can be further extended to electronic and photonic devices.

cond-mat.mes-hall↗

arXiv2020

MIRA: Leveraging Multi-Intention Co-click Information in Web-scale Document Retrieval using Deep Neural Networks

We study the problem of deep recall model in industrial web search, which is, given a user query, retrieve hundreds of most relevance documents from billions of candidates. The common framework is to train two encoding models based on neural embedding which learn the distributed representations of queries and documents separately and match them in the latent semantic space. However, all the exiting encoding models only leverage the information of the document itself, which is often not sufficient in practice when matching with query terms, especially for the hard tail queries. In this work we aim to leverage the additional information for each document from its co-click neighbour to help document retrieval. The challenges include how to effectively extract information and eliminate noise when involving co-click information in deep model while meet the demands of billion-scale data size for real time online inference. To handle the noise in co-click relations, we firstly propose a web-scale Multi-Intention Co-click document Graph(MICG) which builds the co-click connections between documents on click intention level but not on document level. Then we present an encoding framework MIRA based on Bert and graph attention networks which leverages a two-factor attention mechanism to aggregate neighbours. To meet the online latency requirements, we only involve neighbour information in document side, which can save the time-consuming query neighbor search in real time serving. We conduct extensive offline experiments on both public dataset and private web-scale dataset from two major commercial search engines demonstrating the effectiveness and scalability of the proposed method compared with several baselines. And a further case study reveals that co-click relations mainly help improve web search quality from two aspects: key concept enhancing and query term complementary.

cs.IR↗