ABSTRACT
The accurate prediction of alloying effects on the martensitic transition temperature (Ms) is still a big challenge. To investigate the composition-dependent lattice deformation strain and the Ms upon the ß to αâ³ phase transition, we calculate the total energies and transformation strains for two selected Ti-Nb-Al and Ti-Nb-Ta ternaries employing a first-principles method. The adopted approach accurately estimates the alloying effect on lattice strain and the Ms by comparing it with the available measurements. The largest elongation and the largest compression due to the lattice strain occur along ±[011]ß and ±[100]ß, respectively. As compared to the overestimation of the Ms from existing empirical relationships, an improved Ms estimation can be realized using our proposed empirical relation by associating the measured Ms with the energy difference between the ß and αâ³ phases. There is a satisfactory agreement between the predicted and measured Ms, implying that the proposed empirical relation could accurately describe the coupling alloying effect on Ms. Both Al and Ta strongly decrease the Ms, which is in line with the available observations. A correlation between the Ms and elastic modulus, C44, is found, implying that elastic moduli may be regarded as a prefactor of composition-dependent Ms. This work sheds deep light on precisely and directly predicting the Ms of Ti-containing alloys from the first-principles method.
ABSTRACT
MOTIVATION: Accurate annotation of different genomic signals and regions (GSRs) from DNA sequences is fundamentally important for understanding gene structure, regulation and function. Numerous efforts have been made to develop machine learning-based predictors for in silico identification of GSRs. However, it remains a great challenge to identify GSRs as the performance of most existing approaches is unsatisfactory. As such, it is highly desirable to develop more accurate computational methods for GSRs prediction. RESULTS: In this study, we propose a general deep learning framework termed DeepGenGrep, a general predictor for the systematic identification of multiple different GSRs from genomic DNA sequences. DeepGenGrep leverages the power of hybrid neural networks comprising a three-layer convolutional neural network and a two-layer long short-term memory to effectively learn useful feature representations from sequences. Benchmarking experiments demonstrate that DeepGenGrep outperforms several state-of-the-art approaches on identifying polyadenylation signals, translation initiation sites and splice sites across four eukaryotic species including Homo sapiens, Mus musculus, Bos taurus and Drosophila melanogaster. Overall, DeepGenGrep represents a useful tool for the high-throughput and cost-effective identification of potential GSRs in eukaryotic genomes. AVAILABILITY AND IMPLEMENTATION: The webserver and source code are freely available at http://bigdata.biocie.cn/deepgengrep/home and Github (https://github.com/wx-cie/DeepGenGrep/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Subject(s)
Deep Learning , Mice , Cattle , Animals , Drosophila melanogaster/genetics , Genomics/methods , Genome , SoftwareABSTRACT
Density functional theory (DFT) calculations have been performed to explore the gas-phase hydrolysis reaction of mononuclear thorium halide clusters ThX4 (X = F, Cl). We have found that the hydrolysis of ThCl4 is easier than that of ThF4. Furthermore, their hydrolysis reactions favor pathways of direct dehydration of Th(OH)4 instead of further hydrolysis of ThOX2. There are some differences between the hydrolysis of ThCl4 and that of MCl4 (M = Ti, Zr and Hf). The X-HY (X = F, Cl; Y = F, Cl and OH) hydrogen bonds play an important role in the hydrogen transfer process of the hydrolysis reaction. The differences in the steric effects and bonding may be important factors that are related to the disparities in the hydrolysis of the above-mentioned metal halides.
