Search

The investigation of emerging non-toxic perovskite materials has been undertaken to advance the fabrication of environmentally sustainable lead-free perovskite solar cells. This study introduces a machine learning methodology aimed at predicting innovative halide perovskite materials that hold promise for use in photovoltaic applications. The seven newly predicted materials are as follows: CsMnCl$_4$, Rb$_3$Mn$_2$Cl$_9$, Rb$_4$MnCl$_6$, Rb$_3$MnCl$_5$, RbMn$_2$Cl$_7$, RbMn$_4$Cl$_9$, and CsIn$_2$Cl$_7$. The predicted compounds are first screened using a machine learning approach, and their validity is subsequently verified through density functional theory calculations. CsMnCl$_4$ is notable among them, displaying a bandgap of 1.37 eV, falling within the Shockley-Queisser limit, making it suitable for photovoltaic applications. Through the integration of machine learning and density functional theory, this study presents a methodology that is more effective and thorough for the discovery and design of materials.

This work presents a machine learning approach to predict novel perovskite oxide materials in the Pr-Al-O and Pr-Sc-O compound families with the potential for photoluminescence applications. The predicted materials exhibit a large bandgap and high Debye temperature, and have remained unexplored thus far. The predicted compounds (Pr$_3$AlO$_6$, Pr$_4$Al$_2$O$_9$, Pr$_3$ScO$_6$ and Pr$_3$Sc$_5$O$_{12}$) are screened using machine learning approach, which are then confirmed by density functional theory calculations. The study includes the calculation of the bandgap and density of states to determine electronic properties, and the optical absorption and emission spectra to determine optical properties. Mechanical stability of the predicted compounds, as demonstrated by satisfying the Born-Huang criterion. By combining machine learning and density functional theory, this work offers a more efficient and comprehensive approach to materials discovery and design.

Using machine learning (ML) approach, we unearthed a new III-V semiconducting material having an optimal bandgap for high efficient photovoltaics with the chemical composition of Gallium-Boron-Phosphide(GaBP$_2$, space group: Pna2$_1$). ML predictions are further validated by state of the art ab-initio density functional theory (DFT) simulations. The stoichiometric Heyd-Scuseria-Ernzerhof (HSE) bandgap of GaBP$_2$ is noted to 1.65 eV, a close ideal value (1.4-1.5 eV) to reach the theoretical Queisser-Shockley limit. The calculated electron mobility is similar to that of silicon. Unlike perovskites, the newly discovered material is thermally, dynamically and mechanically stable. Above all the chemical composition of GaBP$_2$ are non-toxic and relatively earth-abundant, making it a new generation of PV material. Using ML, we show that with a minimal set of features the bandgap of III-III-V and II-IV-V semiconductor can be predicted up to an RMSE of less than 0.4 eV. We presented a set of scaling laws, which can be used to estimate the bandgap of new III-III-V and II-IV-V semiconductor, with three different crystal phases, within an RMSE of approx. 0.5 eV.

Graphene and phosphorene are monolayer of graphite and phosphorous, respectively. Graphene is completely relativistic (Dirac) fermionic system, but phosphorene is pseudorelativistic fermionic system. In phosphorene, electronic spectrum of phosphorene has a Dirac like (linear) band in one direction and Schrodinger like (parabolic) band in other direction. Conventional Rabi oscillations are studied by using rotating wave approximation in resonance case. The Floquet theory is an alternative way of study Rabi oscillations in off-resonance case and dominating in case of low energy physics. In this article, the nonlinear optical response of graphene and phosphorene studied under intense applied quantized electromagnetic field via Floquet theory. The Bloch-Siegert shift is observed for graphene and phosphorene. A numerical model is applied for justifying the role of anisotropy in phosphorene. Therefore, the Floquet theory can be utilized to characterize the different fermionic systems.

First principles studies were performed in order to find out the possibility of inducing half-metallicity in Heusler Compound CoFeMnSb, by means of alloying it with 3d-transition metal elements. Proper alloying element is selected through the calculations of formation energies. These calculations were tested with different concentrations of alloying elements at different atomic sites. Among the selected transition metal elements Sc and Ti are proposed to be excellent alloying elements particularly at Mn site. By using these alloying elements complete half metallic behaviour is obtained in CoFeMn0.25Sc0.75Sb, CoFeMn0.75Ti0.25Sb, CoFeMn0.625Ti0.375Sb, CoFeMn0.50Ti0.50Sb, CoFeMn0.25Ti0.75Sb and CoFeTiSb alloys. Shifting of Co-Fe d-states towards lower energy region leads to zero density of states at Fermi level for the spin minority channel. Alloying effects on the electronic structure and magnetization are discussed in details. Thermodynamical stability of these new alloys are major part of this study. The Curie temperatures of CoFeMn0.25Sc0.75Sb and CoFeMn0.75Ti0.25Sb were found to be 324.5 K and 682 K; respectively, showing good candidature for spintronics applications. For understanding the bonding nature of constituent atom of CoFeMnSb, crystal orbital Hamiltonian populations have been analysed.

Context-dependence in human cognition process is a well-established fact. Following this, we introduced the image segmentation method that can use context to classify a pixel on the basis of its membership to a particular object-class of the concerned image. In the broad methodological steps, each pixel was defined by its context window (CW) surrounding it the size of which was fixed heuristically. CW texture defined by the intensities of its pixels was convoluted with weights optimized through a non-parametric function supported by a backpropagation network. Result of convolution was used to classify them. The training data points (i.e., pixels) were carefully chosen to include all variety of contexts of types, i) points within the object, ii) points near the edge but inside the objects, iii) points at the border of the objects, iv) points near the edge but outside the objects, v) points near or at the edge of the image frame. Moreover the training data points were selected from all the images within image-dataset. CW texture information for 1000 pixels from face area and background area of images were captured, out of which 700 CWs were used as training input data, and remaining 300 for testing. Our work gives the first time foundation of quantitative enumeration of efficiency of image-segmentation which is extendable to segment out more than 2 objects within an image., Comment: 10 pages, 7 figures, 4 tables, not published anywhere