Reducing time to discovery : materials and molecular modeling, imaging, informatics, and integration

This work was supported by the KAIST-funded Global Singularity Research Program for 2019 and 2020. J.C.A. acknowledges support from the National Science Foundation under Grant TRIPODS + X:RES-1839234 and the Nano/Human Interfaces Presidential Initiative. S.V.K.’s effort was supported by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division and was performed at the Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences (CNMS), a U.S. Department of Energy, Office of Science User Facility.Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing-structure-property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure-property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni-Co-Mn cathode materials illustrates M3I3's approach to creating libraries of multiscale structure-property-processing relationships. We end with a future outlook toward recent developments in the field of M3I3.Peer reviewe

Resilience and Cognitive Bias in Chinese Male Medical Freshmen

BackgroundPsychological resilience has become a hot issue in positive psychology research. However, little is known about cognitive bias difference of individuals with different resilience levels. This study aimed to explore the characteristics of cognitive bias and its role in Chinese medical freshmen with different resilience levels.Methods312 Chinese medical freshmen were surveyed by the Chinese version of Connor–Davidson Resilience Scale, 92 of whom were, respectively, allocated into high (n = 46) and low (n = 46) resilient group to complete computerized tests using an attentional shifting task and an emotional picture recognition task.ResultsAll participants had the highest recognition accuracy toward negative pictures compared to neutral and positive ones. By comparison, it was found that the high-resilient group had a longer recognition response time toward positive emotional pictures, but a shorter response time toward negative emotional pictures, while the low-resilient group had a longer response time toward negative emotional pictures.ConclusionThis study pointed to the association between resilience and cognitive bias. Medical freshmen with different resilience levels showed significant differences in the cognitive bias toward emotional pictures, suggesting that reducing negative cognitive bias and promoting positive cognitive bias could be important targets to increase resilience

A Comparison of Lipid Contents in Different Types of Peanut Cultivars Using UPLC-Q-TOF-MS-Based Lipidomic Study

Peanuts are a rich dietary source of lipids, which are essential for human health. In this study, the lipid contents of 13 peanut cultivars were analyzed using UPLC-Q-TOF-MS and GC–MS. The OXITEST reactor was used to test their lipid oxidation stabilities. A total of 27 subclasses, 229 individual lipids were detected. The combined analysis of lipid and oxidation stability showed that lipid unsaturation was inversely correlated with oxidation stability. Moreover, lipid profiles differed significantly among the different peanut cultivars. A total of 11 lipid molecules (TG 18:2/18:2/18:2, TG 24:0/18:2/18:3, TG 20:5/14:1/18:2, TG 18:2/14:1/18:2, PE 17:0/18:2, BisMePA 18:2/18:2, PG 38:5, PMe 18:1/18:1, PC 18:1/18:1, MGDG 18:1/18:1, TG 10:0/10:1/18:1) might be employed as possible indicators to identify high oleic acid (OA) and non-high OA peanut cultivars, based on the PLS-DA result of lipid molecules with a VIP value greater than 2. This comprehensive analysis will help in the rational selection and application of peanut cultivars

Evidence for natural selection of immune genes from Parachromis managuensis by transcriptome sequencing

Parachromis managuensis is a native cichlid fish from Central America that has been recently introduced to Southern China. The present study aimed to identify the adaptive evolution of P. managuensis using transcriptome data and provided genetic information regarding this novel fish in China. We obtained the transcriptome sequences from a mixed cDNA library of P. managuensis. A total of 19,419,739 raw reads were obtained from the cDNA library. The de novo assembly generated a total of 102,977 unigenes. The genomic data of Atlantic cod (Gadus morhua) and Nile tilapia (Oreochromis niloticus) were used to identify orthologs by phylogenetic analysis. Based on the phylogeny, we detected 105 positively selected genes in P. managuensis from 6197 orthologous unigenes. The GO annotation revealed that the T-box protein 20 (TBX20) in the category ‘Immune system process’ was positively selected and four positively selected genes were found in the ‘Immune system’ pathway by KEGG analysis. Among them, the genes encoding for phosphoinositide 3-kinase adapter protein 1, ras-related protein Rap-1b, complement factor I and probable ATP-dependent RNA helicase DHX58 were associated with immune adaptation. These findings can provide additional information regarding the natural selection of P. managuensis and can be used as a representative example for future comparative transcriptome and immunological studies

Graphene-nanowire hybrid structures for high-performance photoconductive devices

Graphene-CdS nanowire (NW) hybrid structures with high-speed photoconductivity were developed. The hybrid structure was comprised of CdS NWs which were selectively grown in specific regions on a single-layer graphene sheet. The photoconductive channels based on graphene-CdS NW hybrid structures exhibited much larger photocurrents than graphene-based channels and much faster recovery speed than CdS NW network-based ones. Our graphene-CdS NW structures can be useful because they were much faster than commercial CdS film-based photodetectors and had photocurrents large enough for practical applications

Novel deep learning approach for practical applications of indentation

© 2022 The AuthorsThe instrumented indentation technique has been investigated to efficiently evaluate the mechanical responses of materials with few limitations on the shape and size of the specimen. There have been attempts to discover a direct correlation between the stress-strain curve and the indenting load-displacement curve by introducing the concept of representative strain and stress. However, it is still difficult to find relible parameters and to distinguish similar load-displacement curves that correspond to different stress-strain curves with a limited number of experimental datasets. The present study introduces a finite element method (FEM)-based simulation that can output various load-displacement datasets corresponding to intrinsic properties of materials, including strain rate; these datasets are validated using experimental indentation results for diverse metallic materials at different indenting speeds (0.6, 0.9, 1.2 mm/min). In addition, an autoencoder (AE)-shaped artificial neural network (ANN) model is designed to efficiently characterize those datasets. Then, the indenting load-displacement datasets are extracted into effective physically meaningful datasets by introducing a data post-processing procedure. The proposed indentation FEM-AE-shaped ANN model demonstrates that a long-range true stress-strain curve can be attained even from a noisy experimental load-displacement dataset.11Nsciescopu

On Channel State Inference and Prediction Using Observable Variables in 802.11b Network

Abstract—Performance of cross-layer protocols that recommend the relay of corrupted packets to higher layers can be improved significantly by accurately inferring/predicting the bit error rate (BER) in the packets. In practice, higher layers observe the bits only after some hard decision. Hence physical layer link-quality indications, such as the signal strength of each individual bit, are not observable at higher layers. Therefore, it is essential to identify practically observable variables, which can be used for reasonably robust channel state inference/prediction (CSI/CSP). Here, inference specifically refers to estimating the BER in an already received packet, while prediction refers to anticipating the BER in a future packet. In this paper, we note that, in practical 802.11b devices, it is possible to acquire a Signal to Silence Ratio (SSR) indication and measure the Background Traffic Intensity ( ρ) on a per packet basis. This paper, thus presents a measurement-based study that analyzes the utility of SSR and ρ as side-information for CSI/CSP. In this work, we exploit the Method of Types to measure the robustness of the observable side-information. Our analysis and simulations based on an extensive set of actual 802.11b traces exhibit the practical utility of the considered observable variables. Keywords- Channel State Estimation, Cross-layer Protocols I

Graphene-nanowire hybrid structures for high-performance photoconductive devices

Graphene-CdS nanowire (NW) hybrid structures with high-speed photoconductivity were developed. The hybrid structure was comprised of CdS NWs which were selectively grown in specific regions on a single-layer graphene sheet. The photoconductive channels based on graphene-CdS NW hybrid structures exhibited much larger photocurrents than graphene-based channels and much faster recovery speed than CdS NW network-based ones. Our graphene-CdS NW structures can be useful because they were much faster than commercial CdS film-based photodetectors and had photocurrents large enough for practical applications.open112930sciescopu

Lanthanide metal-assisted synthesis of rhombic dodecahedral MNi (M = Ir and Pt) nanoframes toward efficient oxygen evolution catalysis

Mixed metal alloy nanoframeworks have shown a great promise as electrocatalysts in water electrolyzers and fuel cells. Although a limited number of mixed metal alloy nanoframeworks have been synthesized through phase segregation of alloy phases and removal of a component, there remains a strong need for a straightforward and facile synthesis route to this important nanostructure. A wide avenue for nanoframework structures can be opened with a fail-proof method for edge-coating shape-controlled template nanoparticles. Herein, we demonstrate that lanthanide metal chlorides can selectively passivate facets of a Ni nanotemplate, leaving the edges for the growth of a secondary metal (M = Ir, Pt). The edge-deposited metal can be further in situ mixed with the underlying Ni phase to afford rhombic dodecahedral nanoframes of binary alloy phases, namely, IrNi (IrNi-RF) and PtNi (PtNi-RF). IrNi-RF showed excellent electrocatalytic activity for the oxygen evolution reaction (OER) in an acidic electrolyte, requiring and overpotential of only 313.6 mV at 10 mA cm(-2). Furthermore, even after 5000 potential cycles in the OER, IrNi-RF underwent little performance loss with an overpotential of 329.3 mV at 10 mA cm(-2), demonstrating excellent catalytic stability. The presence of highly active grain boundaries, agglomeration-free frame structures, as well as the presence of IrNi/IrOx interface might be responsible for the excellent electrocatalytic activity and stability. © 2017 Elsevier Ltd. All rights reserved.1671sciescopu