scholarly journals Structure Prediction and Mechanical Properties of Silicon Hexaboride on Ab Initio Level

Materials ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 7887
Author(s):  
Tamara Škundrić ◽  
Branko Matović ◽  
Aleksandra Zarubica ◽  
Jelena Zagorac ◽  
Peter Tatarko ◽  
...  

Silicon borides represent very appealing industrial materials for research owing to their remarkable features, and, together with other boride and carbide-based materials, have very wide applications. Various Si–B phases have been investigated in the past, however a limited number of studies have been done on the pristine SiB6 compound. Structure prediction using a data mining ab initio approach has been performed in pure silicon hexaboride. Several novel structures, for which there are no previous experimental or theoretical data, have been discovered. Each of the structure candidates were locally optimized on the DFT level, employing the LDA-PZ and the GGA-PBE functional. Mechanical and elastic properties for each of the predicted and experimentally observed modifications have been investigated in great detail. In particular, the ductility/brittleness relationship, the character of the bonding, Young’s modulus E, bulk modulus B, and shear modulus K, including anisotropy, have been calculated and analyzed.

RSC Advances ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 3577-3581 ◽  
Author(s):  
Nursultan Sagatov ◽  
Pavel N. Gavryushkin ◽  
Talgat M. Inerbaev ◽  
Konstantin D. Litasov

We carried out ab initio calculations on the crystal structure prediction and determination of P–T diagrams within the quasi-harmonic approximation for Fe7N3 and Fe7C3.


2012 ◽  
Vol 717-720 ◽  
pp. 415-418
Author(s):  
Yoshitaka Umeno ◽  
Kuniaki Yagi ◽  
Hiroyuki Nagasawa

We carry out ab initio density functional theory calculations to investigate the fundamental mechanical properties of stacking faults in 3C-SiC, including the effect of stress and doping atoms (substitution of C by N or Si). Stress induced by stacking fault (SF) formation is quantitatively evaluated. Extrinsic SFs containing double and triple SiC layers are found to be slightly more stable than the single-layer extrinsic SF, supporting experimental observation. Effect of tensile or compressive stress on SF energies is found to be marginal. Neglecting the effect of local strain induced by doping, N doping around an SF obviously increase the SF formation energy, while SFs seem to be easily formed in Si-rich SiC.


2021 ◽  
Author(s):  
Tomas Rosén ◽  
Ruifu Wang ◽  
HongRui He ◽  
Chengbo Zhan ◽  
Shirish Chodankar ◽  
...  

During the past decade, cellulose nanofibrils (CNFs) have shown tremendous potential as a building block to fabricate new advanced materials that are both biocompatible and biodegradable. The excellent mechanical properties...


2021 ◽  
Vol 26 (2) ◽  
pp. 39
Author(s):  
Juan P. Sánchez-Hernández ◽  
Juan Frausto-Solís ◽  
Juan J. González-Barbosa ◽  
Diego A. Soto-Monterrubio ◽  
Fanny G. Maldonado-Nava ◽  
...  

The Protein Folding Problem (PFP) is a big challenge that has remained unsolved for more than fifty years. This problem consists of obtaining the tertiary structure or Native Structure (NS) of a protein knowing its amino acid sequence. The computational methodologies applied to this problem are classified into two groups, known as Template-Based Modeling (TBM) and ab initio models. In the latter methodology, only information from the primary structure of the target protein is used. In the literature, Hybrid Simulated Annealing (HSA) algorithms are among the best ab initio algorithms for PFP; Golden Ratio Simulated Annealing (GRSA) is a PFP family of these algorithms designed for peptides. Moreover, for the algorithms designed with TBM, they use information from a target protein’s primary structure and information from similar or analog proteins. This paper presents GRSA-SSP methodology that implements a secondary structure prediction to build an initial model and refine it with HSA algorithms. Additionally, we compare the performance of the GRSAX-SSP algorithms versus its corresponding GRSAX. Finally, our best algorithm GRSAX-SSP is compared with PEP-FOLD3, I-TASSER, QUARK, and Rosetta, showing that it competes in small peptides except when predicting the largest peptides.


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