Accelerating First Principles Calculation of Multi-Component Alloy Steady-State Structure and Elastic Properties in Full Component Space.

The FeNiCrAlCoCuTi alloy system has great advantages in mechanical properties such as high hardness and toughness. It has high performance potential and research value and the key in research is designing alloy compositions with target properties. The traditional method, experimental analysis, is highly inefficient to properly exploit the intrinsic relationship between material characteristics and properties for multi-component alloys, especially in investigating the whole composition space. In this work, we present a research way that uses first principles calculation to obtain the properties of multi-component alloys and uses machine learning to accelerate the research. The FeNiCrAlCoCuTi alloy system with its elastic properties is used as an example to demonstrate this process. We specifically design models for each output, all of which have RMSE values of less than 1.1, and confirm their effectiveness through experimental data in the literature, showing that the relative error is below 5%. Additionally, we perform an interpretable analysis on the models, exposing the underlying relationship between input features and output. By means of spatial transformation, we achieve the prediction of the full-component spatial performance from binary to multiple components. Taking the FeNiCrAlM (M = Co, Cu, Ti) quinary alloy system as an example, we design a single-phase BCC structure composed of an Fe0.23Cr0.23Al0.23Ni0.03Cu0.28 alloy with a Young's modulus of 273.10 GPa, as well as a single-phase BCC structure composed of an Fe0.01Cr0.01Al0.01Ni0.44Co0.53 alloy with a shear modulus of 103.6 GPa. Through this research way, we use machine learning to accelerate the calculation, which greatly shortens research time and costs. This work overcomes the drawbacks of traditional experiments and directly obtains element compositions and composition intervals with excellent performance.

Investigation of specific interactions between T7 promoter and T7 RNA polymerase by force spectroscopy using atomic force microscope.

The specific recognition and binding of promoter and RNA polymerase is the first step of transcription initiation in bacteria and largely determines transcription activity. Therefore, direct analysis of the interaction between promoter and RNA polymerase in vitro may be a new strategy for promoter characterization, to avoid interference due to the cell's biophysical condition and other regulatory elements. In the present study, the specific interaction between T7 promoter and T7 RNA polymerase was studied as a model system using force spectroscopy based on atomic force microscope (AFM). The specific interaction between T7 promoter and T7 RNA polymerase was verified by control experiments, and the rupture force in this system was measured as 307.2 ± 6.7 pN. The binding between T7 promoter mutants with various promoter activities and T7 RNA polymerase was analyzed. Interaction information including rupture force, rupture distance and binding percentage were obtained in vitro, and reporter gene expression regulated by these promoters was also measured according to a traditional promoter activity characterization method in vivo Using correlation analysis, it was found that the promoter strength characterized by reporter gene expression was closely correlated with rupture force and the binding percentage by force spectroscopy. These results indicated that the analysis of the interaction between promoter and RNA polymerase using AFM-based force spectroscopy was an effective and valid approach for the quantitative characterization of promoters.