Quantum support vector classifier for phase diagram prediction in quinary systems.

The integration of machine learning (ML) in materials science has accelerated the discovery and optimization of novel materials. However, classical ML approaches often face limitations in handling the increasing complexity and scale of modern datasets. Quantum machine learning (QML), leveraging quantum computing principles, offers a promising avenue to address these challenges.

Advancing 2D material predictions: superior work function estimation with atomistic line graph neural networks.

Despite the increased research and scholarly attention on two-dimensional (2D) materials, there is still a limited range of practical applications for these materials. This is because it is challenging to acquire properties that are usually obtained by experiments or first-principles predictions, which require substantial time and resources. Descriptor-based machine learning models frequently require further density functional theory (DFT) calculations to enhance prediction accuracy due to the intricate nature of the systems and the constraints of the descriptors employed.

Transition Metal Anchored Novel Holey Boron Nitride Analogues as Single-Atom Catalysts for the Hydrogen Evolution Reaction.

The increasing global energy demand and environmental pollution necessitate the development of alternative, sustainable energy sources. Hydrogen production through electrochemical methods offers a carbon-free energy solution. In this study, we have designed novel boron nitride analogues (BNyne) and investigated their stability and electronic properties.

DFT and machine learning guided investigation into the design of new dual-atom catalysts based on α-2 graphyne.

The realm of atomic catalysts has witnessed notable advancements; yet, the predominant focus remains on single atomic catalysts (SACs). The exploration and successful implementation of dual atomic catalysts (DACs) pose intricate challenges, primarily concerning thermodynamic stability and optimal metallic composition. To address these issues, we present a comprehensive theoretical investigation of α-2 graphyne (GPY)-based DACs, synthesized in-house with a keen focus on formation stability.

Bayesian Optimization for Efficient Prediction of Gas Uptake in Nanoporous Materials.

The discovery and optimization of novel nanoporous materials (NPMs) such as Metal-Organic Frameworks (MOFs) and Covalent Organic Frameworks (COFs) are crucial for addressing global challenges like climate change, energy security, and environmental degradation. Traditional experimental approaches for optimizing these materials are time-consuming and resource-intensive. This research paper presents a strategy using Bayesian optimization (BO) to efficiently navigate the complex design spaces of NPMs for gas storage applications.

Exploring Hydrogen Storage Capacity in Metal-Organic Frameworks: A Bayesian Optimization Approach.

Metal-organic Frameworks (MOFs) can be employed for gas storage, capture, and sensing. Finding the MOF with the best adsorption property from a large database is usual for adsorption calculations. In high-throughput computational research, the expense of computing thermodynamic quantities limits the finding of MOFs for separations and storage.

Structural and Electronic Properties of Two-Dimensional Materials: A Machine-Learning-Guided Prediction.

The growing number of studies and interest in two-dimensional (2D) materials has not yet resulted in a wide range of material applications. This is a result of difficulties in getting the properties, which are often determined through numerical experiments or through first-principles predictions, both of which require lots of time and resources. Here we provide a general machine learning (ML) model that works incredibly well as a predictor for a variety of electronic and structural properties such as band gap, fermi level, work function, total energy and area of unit cell for a wide range of 2D materials derived from the Computational 2D Materials Database (C2DB).

Acetylene-Mediated Borophosphene Dirac Materials as Efficient Anode Materials for Lithium-Ion Batteries.

Generally, graphynes have been generated by the insertion of acetylenic content (-C≡C-) in the graphene network in different ratios. Also, several aesthetically pleasing architectures of two-dimensional (2D) flatlands have been reported with the incorporation of acetylenic linkers between the heteroatomic constituents. Prompted by the experimental realization of boron phosphide, which has provided new insights on the boron-pnictogen family, we have modelled novel forms of acetylene-mediated borophosphene nanosheets by joining the orthorhombic borophosphene stripes with different widths and with different atomic constituents using acetylenic linkers.

Deciphering the Role of Substitution in Transition-Metal Phosphorous Trisulfide (100) Surface: A Highly Efficient and Durable Pt-free ORR Electrocatalyst.

The rational design and development of earth-abundant, cost-effective, environmentally benign, and highly robust oxygen reduction reaction (ORR) electrocatalysts can circumvent the obstacles associated with the large-scale commercialization of fuel cells. Here, using first-principles-based density functional theory (DFT), we have computationally screened the potential and feasibility of transition-metal phosphorous trisulfides (TMPS ) (100) surfaces as efficient ORR electrocatalyst in acidic fuel cell application. MnPS (100) surface emerges to be the best among TMPS surfaces with optimal O activation resulting in very low overpotential.

Evolutionary structure prediction-assisted design of anode materials for Ca-ion battery based on phosphorene.

The utilization of multivalent ions such as Ca(ii), Mg(ii), and Al(iii) in energy storage devices opens up new opportunities to store energy density in a more efficient manner rather than monovalent Li or Na ion batteries. Active research on Ca(ii) has been limited due to the low diffusion rate of Ca within the lattice as well as the difficulty of the reversible electrodeposition of Ca in standard electrolytes at room temperature. Herein, using first-principles calculations, we have studied the applications of various allotropes of phosphorene (Pn) as potential materials for Ca(ii) battery (CIB).

Transition-Metal Phosphorus Trisulfides and its Vacancy Defects: Emergence of a New Class of Anode Material for Li-Ion Batteries.

In the search of suitable anode candidates with high specific capacity, favorable potential, and structural stability for lithium-ion batteries (LIBs), transition-metal phosphorus trisulfides (TMPS ) can be considered as one of the most promising alternatives to commercial graphite. Here, it was demonstrated that the limitations of commercial anode materials (i.e.

Doped boron nitride surfaces: potential metal free bifunctional catalysts for non-aqueous Li-O batteries.

The development of novel cathode catalysts is crucial for the practical application of lithium-oxygen (Li-O2) batteries. In this paper, we have evaluated the catalytic mechanism and activity of doped hexagonal boron nitride (h-BN) surfaces as cathode catalysts for nonaqueous Li-O2 batteries. From the free energy diagrams it is evident that the CN doped h-BN surface shows the best catalytic activity among the others and this arises due to its considerably lower oxygen reduction reaction (ORR) overpotential and lower oxygen evolution reaction (OER) overpotential.

Differences between adult and pediatric onset Henoch-Schonlein purpura from North India.

Aim: Henoch-Schönlein purpura (HSP), a primary vasculitis, characterized by purpura, abdominal pain, arthritis and renal involvement, is predominantly a disease of childhood. However, rarely it can occur in adults in whom it is believed to be a more severe form with poor renal outcomes. We aimed to answer if the age of onset affected the clinical spectrum and renal outcomes of the disease in a north Indian population.

Density functional theory study of defective silicenes as anode materials for lithium ion batteries.

In this contribution, we explore Li adsorption and diffusion on defective silicenes using first principles calculations. Defect formation energy (E) values showed that silicenes with 5105 and 5559 vacancy defects (Si-5559 and Si-5105) are likely to form during the fabrication process and E values are about one-third of graphenes. Calculation of Li adsorption energy indicated that Si-5559 and Si-5105 are better than pristine silicene for Li dispersion in the half-lithiated state.

Exotic Physics and Chemistry of Two-Dimensional Phosphorus: Phosphorene.

Phosphorene, the monolayer form of black phosphorus, is the most recent addition to graphene-like van der Waals two-dimensional (2D) systems. Due to its several interesting properties, namely its tunable direct band gap, high carrier mobility, and unique in-plane anisotropy, it has emerged as a promising candidate for electronic and optoelectronic devices. Phosphorene (Pn) reveals a much richer phase diagram than graphene, and it comprises the two forms namely the stapler-clip like (black Pn, α form) and chairlike (blue Pn, β form) structures.

Steric and electric field driven distortions in aromatic molecules: spontaneous and non-spontaneous symmetry breaking.

The structures of molecules form the cornerstone of our chemical knowledge. Lowering of symmetry in closed-shell molecules is often attributed to the Pseudo Jahn-Teller (PJT) distortions wherein non-adiabatic coupling (NAC) between the ground state and excited states creates vibrational instability along specific normal modes. Nevertheless, other factors like steric interactions are also well known in the literature to induce structural distortions.

Pseudo-Jahn-Teller Distortion in Two-Dimensional Phosphorus: Origin of Black and Blue Phases of Phosphorene and Band Gap Modulation by Molecular Charge Transfer.

Phosphorene (Pn) is stabilized as a layered material like graphite, yet it possess a natural direct band gap (Eg = 2.0 eV). Interestingly, unlike graphene, Pn exhibits a much richer phase diagram which includes distorted forms like the stapler-clip (black Pn, α form) and chairlike (blue Pn, β form) structures.