A5026588242- Machine Learning in Materials Science
- X-ray Diffraction in Crystallography
- Big Data and Digital Economy
- Advanced Battery Materials and Technologies
- Transition Metal Oxide Nanomaterials
- Advancements in Battery Materials
- Electronic and Structural Properties of Oxides
- Nuclear Materials and Properties
- Inorganic Chemistry and Materials
- Graphene research and applications
- 2D Materials and Applications
- Gas Sensing Nanomaterials and Sensors
- Quantum and electron transport phenomena
- Advanced Chemical Physics Studies
- ZnO doping and properties
- Hydrogen embrittlement and corrosion behaviors in metals
- Advanced Materials Characterization Techniques
- Catalysis and Oxidation Reactions
- Computational Drug Discovery Methods
- Theoretical and Computational Physics
- Advanced Memory and Neural Computing
- Neural Networks and Applications
- Crystallization and Solubility Studies
- Advanced Graph Neural Networks
- Complex Network Analysis Techniques
Computer Network Information Center
2015-2025
Chinese Academy of Sciences
2015-2025
University of Chinese Academy of Sciences
2021-2025
Institute of Theoretical Physics
2012-2014
Southwest University
2011
Baotou Teachers College
2008
Using a comprehensive structure search and high-throughput first-principles calculations of 1483 compounds, this study presents the phase diagram Lu-H-N. The formation energy landscape Lu-H-N was derived utilized to assess thermodynamic stability compounds. Results indicate that there are no stable ternary structures in system, but metastable structures, such as Lu20H2N17 (C2/m), Lu2H2N (P3-m1), were observed with small Ehull (< 100 meV/atom). Moreover, applying hydrostatic pressure up 10...
A correct understanding of the effects dopants and electric field on metal–insulator transition VO2 remains a challenge. Herein, theoretical experimental studies are performed to elucidate role W transition. found introduce additional localized electrons in d bands, which induce splitting d// orbitals V–V dimerization local V ions W-doped R-VO2. The experiments electric-field-driven MIT nanofilms indicate that conductivity R-VO2 increases with increasing applied voltage; however, for pure...
Crystal structure predictions based on the combination of first-principles calculations and machine learning have achieved significant success in materials science. However, most these approaches are limited to predicting specific systems, which hinders their application unknown or unexplored domains. In this paper, we present CrySPAI, a crystal prediction package developed using artificial intelligence (AI) predict energetically stable structures inorganic given chemical compositions. The...
Crystal structure forms the foundation for understanding physical and chemical properties of materials. Generative models have emerged as a new paradigm in crystal prediction(CSP), however, accurately capturing key characteristics structures, such periodicity symmetry, remains significant challenge. In this paper, we propose Transformer-Enhanced Variational Autoencoder Structure Prediction (TransVAE-CSP), who learns characteristic distribution space stable materials, enabling both...
Crystal structure predictions based on the combination of first-principles calculations and machine learning have achieved significant success in materials science. However, most these approaches are limited to predicting specific systems, which hinders their application unknown or unexplored domains. In this paper, we present a crystal prediction software artificial intelligence, named as CrySPAI, predict energetically stable structures inorganic given chemical compositions. The consists...
Abstract National Materials Data Management and Service platform (NMDMS) is a materials data repository for the publication sharing of heterogeneous scientific follows FAIR principles: Findable, Accessible, Interoperable, Reusable. To ensure are ‘Interoperable, NMDMS uses user-friendly semi-structured model, named dynamic container’, to define, exchange, store data. Then, personalized yet standardized submission subsystem, rigorous project review multi-granularity query retrieval subsystem...
Driven by emerging research paradigms, the application of artificial intelligence models presents innovative tools for designing materials and optimizing their performance. In field science, there is a current emphasis on exploring techniques characterizing material structures to achieve precise descriptions. This paper proposes crystal graph convolution neural network model that incorporates tripartite interaction approach. The not only atomic information, bond lengths, angles but also...
The formation mechanism of the metastable M2-phase VO2, which is believed to be a true Mott insulator, has attracted great attention for understanding intriguing physics metal–insulator transition VO2 and promising application in ultrafast electronic switching devices. Herein, we conducted hole-doping calculation regardless type element revealed theoretically that hole carriers disentangle complex Mott–Peierls relevance states M1-phase VO2. induces zigzag dimerized V–V chains separate into...
Abstract The fourth paradigm of science has achieved great success in material discovery and it highlights the sharing interoperability data. However, most data are scattered among various research institutions, a big transmission will consume significant bandwidth tremendous time. At meanwhile, some owners prefer to protect keep their initiative cooperation. This dilemma gradually leads “data island” problem, especially science. To attack problem make full use data, we propose new strategy...
Objective: The aim of this study was to investigate the association between CD14 gene promoter SNPs with serum total-IgE and eosinophil levels in atopic asthma non-atopic Chinese Han. Methods: A total 152 patients were divided into (n = 100) 52) groups for study. Six analyzed using PCR sequencing. Serum measured. genotype frequencies loci evaluated by ANOVA test method. Hundred sixteen healthy subjects constitute control group. Results: We found that significantly higher individuals when...
The phonon thermal contribution to the melting temperature of nano-particles is inspected. discrete summation states and its corresponding integration form as an approximation for a nano-particle or bulk system have been analyzed. energy levels pure size effect wave-vector shifts boundary conditions are investigated in detail. Unlike macroscopic thermodynamics, volume zero-mode not zero, it plays important role condition effect. We find that will rising due purely finite effect; lower bound...