Composition and Structure Based GGA Bandgap Prediction Using Machine Learning Approach

This study focuses on developing precise machine learning (ML) regression models for predicting energy bandgap values based on chemical compositions and crystal structures. The primary aim is to match the accuracy of predictions derived from GGA-PBE calculations and validate them through density functional theory (DFT)-based band structure calculations. We assessed eight standalone ML regression models, including AdaBoost, Bagging, CatBoost, LGBM, RF, DT, GB, and XGB. These models were analyzed for their ability to predict GGA-PBE bandgap values across diverse material structures and compositions, using a dataset containing bandgap values for 106,113 compounds. Additionally, we constructed four ensemble models using the stacking method and seven using the bagging method. These ensemble models incorporated RidgeCV and LassoCV to explore if ensemble techniques could enhance prediction accuracy. The dataset was divided into subsets of varying sizes: 10,000, 25,000, 50,000, and 100,000 entries. We determined feature importance through permutation techniques and established a correlation coefficient matrix using the Pearson correlation method. The Random Forest (RF) model emerged as the top performer among standalone models, achieving an R2 value of 0.943 and an RMSE value of 0.504 eV. Bagging regression demonstrated improved performance across different dataset sizes with streamlined feature selection. Ensemble models, particularly bagging, consistently outperformed standalone models, achieving the best R2 value of 0.948 and an RMSE value of 0.479 eV in the test dataset. Using the best-performing model, we predicted bandgap values for new half-Heusler compounds with 18 valence electron counts. These predictions were successfully validated using accurate DFT calculations. DFT calculations indicated that the newly predicted compounds are narrow bandgap semiconductors with dynamic stability.Comment: 17 pages, 17 figures, Research pape

Accelerating Discovery of Vacancy Ordered 18-Valence Electron Half-Heusler Compounds: A Synergistic Approach of Machine Learning and Density Functional Theory

In this study, we attempted to model vacancy ordered half Heusler compounds with 18 valence electron count (VHH) derived from 19 VEC compounds such as TiNiSb such that the compositions will be Ti0.75NiSb, Zr0.75NiSb and Hf0.75NiSb with semiconducting behavior. The main motivation is that such a vacancy-ordered phase not only introduces semi conductivity but also it disrupts the phonon conducting path in HH alloys and thus reduces the thermal conductivity and as a consequence enhances the thermoelectric figure of merit. In order to predict the formation energy ({\Delta}Hf) from composition and crystal structure we have used 4684 compounds for their {\Delta}Hf values are available in the material project database and trained a machine learning model with R2 value of 0.943. Using this trained model, we have predicted the {\Delta}Hf of a list of VHH. From the predicted database of VHH we have selected Zr0.75NiSb and Hf0.75NiSb to validate the machine learning prediction using accurate DFT calculation. The calculated {\Delta}Hf for these two compounds from DFT calculation are found to be comparable with our ML prediction. The calculated electronic and lattice dynamics properties show that these materials are narrow band gap semiconductors and are dynamically stable as their all-phonon dispersion curves are having positive frequencies. The calculated Seebeck coefficient, electrical conductivity as well as thermal conductivity, power factor and thermoelectric figure of merit are analyzed.Comment: 5 pages, 2 figures, conferenc

ENHANCED REAL-TIME GROUP AUCTION SYSTEM FOR EFFICIENT ALLOCATION OF CLOUD INTERNET APPLICATIONS

Cloud internet applications have recently attracted a large number of users in the Internet. With the invention of these cloud internet applications, it is inefficient to allocate maximum number of participants in real time group auction system. So an efficient approximation algorithm is proposed with the improved combinatorial double auction protocol. It is developed to enable different kinds of resource distribution among multiple users and providers. At the same time it includes more number of participants in an auction. Due to the NP-hardness of binary integer programming for resource distribution in a real time group auction system, the improved approximation algorithm is proposed to deal with np-hardness and to obtain the optimal solution. Participant honesty is necessary to ensure auction trustfulness

Isolation and Characterization of Bacteria from the Gut of Bombyx mori that Degrade Cellulose, Xylan, Pectin and Starch and Their Impact on Digestion

Bombyx mori L. (Lepidoptera: Bombycidae) have been domesticated and widely used for silk production. It feeds on mulberry leaves. Mulberry leaves are mainly composed of pectin, xylan, cellulose and starch. Some of the digestive enzymes that degrade these carbohydrates might be produced by gut bacteria. Eleven isolates were obtained from the digestive tract of B. mori, including the Gram positive Bacillus circulans and Gram negative Proteus vulgaris, Klebsiella pneumoniae, Escherichia coli, Citrobacter freundii, Serratia liquefaciens, Enterobacter sp., Pseudomonas fluorescens, P. aeruginosa, Aeromonas sp., and Erwinia sp.. Three of these isolates, P. vulgaris, K. pneumoniae, C. freundii, were cellulolytic and xylanolytic, P. fluorescens and Erwinia sp., were pectinolytic and K. pneumoniae degraded starch. Aeromonas sp. was able to utilize the CMcellulose and xylan. S. liquefaciens was able to utilize three polysaccharides including CMcellulose, xylan and pectin. B. circulans was able to utilize all four polysaccharides with different efficacy. The gut of B. mori has an alkaline pH and all of the isolated bacterial strains were found to grow and degrade polysaccharides at alkaline pH. The number of cellulolytic bacteria increases with each instar