다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

비만아 부모의 돌봄 경험: q 방법론

The overwhelming majority of scientific knowledge is published as text, which is difficult to analyse by either traditional statistical analysis or modern machine learning methods. By contrast, the main source of machine-interpretable data for the materials research community has come from structured property databases1,2, which encompass only a small fraction of the knowledge present in the research literature. Beyond property values, publications contain valuable knowledge regarding the connections and relationships between data items as interpreted by the authors. To improve the identification and use of this knowledge, several studies have focused on the retrieval of information from scientific literature using supervised natural language processing3-10, which requires large hand-labelled datasets for training. Here we show that materials science knowledge present in the published literature can be efficiently encoded as information-dense word embeddings11-13 (vector representations of words) without human labelling or supervision. Without any explicit insertion of chemical knowledge, these embeddings capture complex materials science concepts such as the underlying structure of the periodic table and structure-property relationships in materials. Furthermore, we demonstrate that an unsupervised method can recommend materials for functional applications several years before their discovery. This suggests that latent knowledge regarding future discoveries is to a large extent embedded in past publications. Our findings highlight the possibility of extracting knowledge and relationships from the massive body of scientific literature in a collective manner, and point towards a generalized approach to the mining of scientific literature.

/pdf/unsupervised-word-embeddings-capture-latent-knowledge-from-28fs54yqwx.pdf

Unsupervised word embeddings capture latent knowledge from materials science literature

Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. Recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep-learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science. The application of DL methods in materials science presents an exciting avenue for future materials discovery and design. 

/pdf/recent-advances-and-applications-of-deep-learning-methods-in-47f5g19w.pdf

Recent advances and applications of deep learning methods in materials science

Using first-principles calculations, the stabilities, electronic structures and piezoelectric properties of two-dimensional (2D) tin dichalcogenide derived Janus SnXY monolayers (X = O, S, Se, Te; Y = O, S, Se, Te; X ≠ Y) are systematically investigated. Apart from the structural instability of SnOTe, all the other Janus monolayers are proved to be thermodynamically, dynamically and mechanically stable. The electronic property calculations demonstrate that Te-participated Janus systems are always metallic and the other systems have a band gap in the range of 0.33–1.74 eV. The appropriate valence band maximum positions guarantee these semiconducting Janus monolayers to be applied as effective catalysts for the oxygen reduction reaction. Due to losing the inversion symmetry, Janus materials exhibit piezoelectric properties. Surprisingly, the largest predicted in-plane piezoelectric coefficient d11 for SnOSe is as high as 27.3 pm V−1, which is one order of magnitude greater than those of reported fashionable piezoelectric materials, such as quartz, MoS2, etc. Our results indicate that 2D tin dichalcogenide derived Janus monolayers are strong candidates for future atomically thin piezoelectric applications in the fields of transducers, sensors, and energy harvesting devices.

Stabilities, and electronic and piezoelectric properties of two-dimensional tin dichalcogenide derived Janus monolayers

2H phase (trigonal prismatic D3h ) of layered two-dimensional (2D) transition metal dichalcogenides (TMDs) have attracted a lot of interests due to the superior electronic and optoelectronic properties. However, flexible electronic devices and thermoelectric performances based on 2H phase have been potentially limited by the strain sensitive electronic band gap and high lattice thermal conductivity ( κ L ). Here, we predict and calculate two 1T (octahedral Oh ) phase monolayer telluride materials SnTe 2 and SiTe2 with soft mechanics, ultralow κ L and electronic properties. The calculated in-plane Young's modulus of monolayer SnTe 2 is softer than most of 1T-MX2 compounds. Furthermore, monolayer SiTe2 and SnTe2 also have relatively flexible electronic properties under large biaxial strain, indicating potential flexible electrode materials. Meanwhile, monolayer SiTe 2 and SnTe2 both exhibit ultralow κ L (2.27 W/mK of SiTe2 and 1.62 W/mK of SnTe2) at room temperature. Considering both acoustic and polar optical phonon scattering of the electronic relaxation time, the figure of merit (ZT) can achieve 0.46 at 600 K and 0.71 at 900 K for monolayer SiTe2 and SnTe2 respectively.

Ultralow lattice thermal conductivity and electronic properties of monolayer 1T phase semimetal SiTe2 and SnTe2

Materials with either high or low lattice thermal conductivity are remarkable for thermal management with applying in high-power electronic, optoelectronic, and thermoelectric devices. The realization of thermal switch between high and low thermal conductivities can greatly promote the ability of thermal energy control. Here, based on first-principles calculations, we propose that ferroelastic PdSe2 can achieve continuous switchable thermal conductivity through strain-driven structural phase transition. Thermal switch we explored mainly stems from soft mechanical properties and strong anharmonicity of the structure after ferroelastic phase transition. We demonstrate that the maximum ratio of thermal switch can reach an order of magnitude, indicating PdSe2 as a promising candidate in thermal devices.

Strain-Driven Switchable Thermal Conductivity in Ferroelastic PdSe2.

Directional design of functional materials with multi-objective constraints is a big challenge, whose performance and stability are determined by different physics factors entangled with each other complicatedly. In this work, we apply the multi-objective optimization based on the Pareto Efficiency and Particle-Swarm Optimization methods to design new functional materials directionally. As a demonstration, we achieve the thermoelectric design of 2D SnSe materials through the methods. We identify several novel metastable 2D SnSe structures with simultaneously lower free energy and better thermoelectric performance over the experimentally-reported monolayer structures. We hope our results about the multi-objective Pareto Optimization method can make a step towards the integrative design of multi-objective and multi-functional materials in the future.

Directional Design of Materials Based on the Multi-Objective Optimization: A Case Study of Two-Dimensional Thermoelectric SnSe

Directional Design of Materials Based on Multi-Objective Optimization: A Case Study of Two-Dimensional Thermoelectric SnSe


 The passive radiative cooling technology shows a great potential application on reducing the enormous global energy consumption. The multilayer metamaterials could enhance the radiative cooling performance. However, it is a challenge to design the radiative cooler. In this work, based on the particle swarm optimization (PSO) evolutionary algorithm, we develop an intelligent workflow in designing photonic radiative cooling metamaterials. Specifically, we design two 10-layer SiO2 radiative coolers doped by cylindrical MgF2 or Air impurities, possessing high emissivity within the selective (8-13 µm) and broadband (8-25 µm) atmospheric transparency window, respectively. Our two kinds of coolers demonstrate power density as high as 119 W/m2 and 132 W/m2 at the room temperature (300 K). Our scheme does not rely on the usage of special materials, forming high-performing metamaterials with conventional poor-performing components. This significant improvement of the emission spectra proves the effectiveness of our inverse design algorithm in boosting the discovery of high-performing functional metamaterials.

AI Designed Radiative Cooling Metamaterials for Passive Thermal Management by Particle Swarm Optimization

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