Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical Learning

Kriging aims at estimating the attributes of unsampled geo-locations from observations in the spatial vicinity or physical connections, which helps mitigate skewed monitoring caused by under-deployed sensors. Existing works assume that neighbors' information offers the basis for estimating the attributes of the unobserved target while ignoring non-neighbors. However, non-neighbors could also offer constructive information, and neighbors could also be misleading. To this end, we propose ``Contrastive-Prototypical'' self-supervised learning for Kriging (KCP) to refine valuable information from neighbors and recycle the one from non-neighbors. As a pre-trained paradigm, we conduct the Kriging task from a new perspective of representation: we aim to first learn robust and general representations and then recover attributes from representations. A neighboring contrastive module is designed that coarsely learns the representations by narrowing the representation distance between the target and its neighbors while pushing away the non-neighbors. In parallel, a prototypical module is introduced to identify similar representations via exchanged prediction, thus refining the misleading neighbors and recycling the useful non-neighbors from the neighboring contrast component. As a result, not all the neighbors and some of the non-neighbors will be used to infer the target. To encourage the two modules above to learn general and robust representations, we design an adaptive augmentation module that incorporates data-driven attribute augmentation and centrality-based topology augmentation over the spatiotemporal Kriging graph data. Extensive experiments on real-world datasets demonstrate the superior performance of KCP compared to its peers with 6% improvements and exceptional transferability and robustness. The code is available at https://github.com/bonaldli/KCPComment: Accepted in AISTATS 202

Screening Deep Eutectic Solvents for CO2 Capture With COSMO-RS

In this work, 502 experimental data for CO2 solubilities and 132 for Henry’s constantsof CO2 in DESs were comprehensively summarized from literatures and used for furtherverification and development of COSMO-RS. Large systematic deviations of 62.2, 59.6,63.0, and 59.1% for the logarithmic CO2 solubilities in the DESs (1:2, 1:3, 1:4, 1:5),respectively, were observed for the prediction with the original COSMO-RS, while thepredicted Henry’s constants of CO2 in the DESs (1:1.5, 1:2, 1:3, 1:4, 1:5) at temperaturesranging of 293.15–333.15 K are more accurate than the predicted CO2 solubility withthe original COSMO-RS. To improve the performance of COSMO-RS, 502 data pointsof CO2 solubility in the DESs (1:2, 1:3, 1:4, 1:5) were used for correcting COSMO-RSwith a temperature-pressure dependent parameter, and the CO2 solubility in the DES(1:6) was predicted to further verify the performance of the corrected model. The resultsindicate that the corrected COSMO-RS can significantly improve the model performancewith the ARDs decreasing down to 6.5, 4.8, 6.5, and 4.5% for the DESs (1:2, 1:3, 1:4, and 1:5), respectively, and the corrected COSMO-RS with the universal parameters can beused to predict the CO2 solubility in DESs with different mole ratios, for example, for theDES (1:6), the corrected COSMO-RS significantly improves the prediction with an ARD of10.3% that is much lower than 78.2% provided by the original COSMO-RS. Additionally,the result from COSMO-RS shows that the σ-profiles can reflect the strength of molecularinteractions between an HBA (or HBD) and CO2, determining the CO2 solubility, and thedominant interactions for CO2 capture in DESs are the H-bond and Van der Waals force,followed by the misfit based on the analysis of the predicted excess enthalpies.Validerad;2020;Nivå 2;2020-02-24 (johcin)</p

Capacity retention behavior and morphology evolution of SixGe1-x nanoparticles as lithium-ion battery anode

Engineering silicon into nanostructures has been a well-adopted strategy to improve the cyclic performance of silicon as a lithium-ion battery anode. Here, we show that the electrode performance can be further improved by alloying silicon with germanium. We have evaluated the electrode performance of SixGe1-x nanoparticles (NPs) with different compositions. Experimentally, SixGe1-x NPs with compositions approaching Si50Ge50 are found to have better cyclic retention than both Si-rich and Ge-rich NPs. During the charge/discharge process, NP merging and Si-Ge homogenization are observed. In addition, a distinct morphology difference is observed after 100 cycles, which is believed to be responsible for the different capacity retention behavior. The present study on SixGe1-x alloy NPs sheds light on the development of Si-based electrode materials for stable operation in lithium-ion batteries (e.g., through a comprehensive design of material structure and chemical composition). The investigation of composition-dependent morphology evolution in the delithiated Li-SiGe ternary alloy also significantly broadens our understanding of dealloying in complex systems, and it is complementary to the well-established understanding of dealloying behavior in binary systems (e.g., Au-Ag alloys)close1