Search for: All records

Unraveling mechanical properties from fundamentals is far from complete despite their vital role in determining applicability and longevity for a given material. Here, we perform a comprehensive study related to mechanical properties of 60 pure elements in bcc, fcc, hcp, and/or diamond structures by means of pure alias shear and pure tensile deformations via density functional theory (DFT) based calculations alongside a broad review of existing literature. The present data compilation enables a detailed correlation analysis of mechanical properties, focusing on DFT-based ideal shear and tensile strengths (τis and σit), stable and unstable stacking fault energies (γsf and γus), surface energy (γs), and vacancy activation energy (QV); and experimental hardness (HB), ultimate tensile strength (σUT), fracture toughness (KIc), and elongation (εEL). The present work examines models, identifies outliers, and provides insights into mechanical properties, for example, (i) HB is correlated by QV, σUT by γs or γus, and KIc by γs; (ii) data outliers are identified for Cr (related to τis, γs, QV, and σUT), Be (τis, γsf, γus, and QV), Hf (HB and KIc), Yb (all properties), and Pt (γsf vs. γus); and (iii) τis σit, γsf, γus, γs, QV, and HB are highly correlated to elemental attributes, while σUT, KIc, and especially εEL are less correlated due mainly to experimental uncertainty. In particular, the present data compilation provides a solid foundation to model properties such as γs and τis of multicomponent alloys and τis of unstable structures like bcc Ti, Zr, and Hf. 

Spatio-temporal deep learning has drawn a lot of attention since many downstream real-world applications can benefit from accurate predictions. For example, accurate prediction of heavy rainfall events is essential for effective urban water usage, flooding warning, and mitigation. In this paper, we propose a strategy to leverage spatially connected real-world features to enhance prediction accuracy. Specifically, we leverage spatially connected real-world climate data to predict heavy rainfall risks in a broad range in our case study. We experimentally ascertain that our Trans-Graph Convolutional Network (TGCN) accurately predicts heavy rainfall risks and real estate trends, demonstrating the advantage of incorporating external spatially-connected real-world data to improve model performance, and it shows that this proposed study has a significant potential to enhance spatio-temporal prediction accuracy, aiding in efficient urban water usage, flooding risk warning, and fair housing in real estate. 

A natural language interface (NLI) to databases is an interface that translates a natural language question to a structured query that is executable by database management systems (DBMS). However, an NLI that is trained in the general domain is hard to apply in the spatial domain due to the idiosyncrasy and expressiveness of the spatial questions. Inspired by the machine comprehension model, we propose a spatial comprehension model that is able to recognize the meaning of spatial entities based on the semantics of the context. The spatial semantics learned from the spatial comprehension model is then injected to the natural language question to ease the burden of capturing the spatial-specific semantics. With our spatial comprehension model and information injection, our NLI for the spatial domain, named SpatialNLI, is able to capture the semantic structure of the question and translate it to the corresponding syntax of an executable query accurately. We also experimentally ascertain that SpatialNLI outperforms state-of-the-art methods.