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Electrocatalytic hydrogen evolution reaction (HER) in alkaline media is a promising electrochemical energy conversion strategy. Ruthenium (Ru) is an efficient catalyst with a desirable cost for HER, however, the sluggish H2O dissociation…
This study investigates the structural, electronic, and catalytic properties of pristine and doped C$_4$N$_2$ nanosheets as potential electrocatalysts for the hydrogen evolution reaction. The pristine C$_{36}$N$_{18}$ nanosheets exhibit…
Designing efficient, metal free, and in-expensive catalyst for electrochemical hydrogen evolution reaction (HER) is crucial for large scale clean and green energy production. Recently synthesized 1D Biphenylene nanoribbons (BPRs) display…
The present study investigates the catalytic performance of a $hg-C_{3}N_{4}$ quantum dot aimed at enhancing electrochemical water splitting, using the first-principles density functional theory. The size of the considered quantum dot lies…
H$_2$ evolution reaction (HER) requires an electrocatalyst to reduce the reaction barriers for the efficient production of H$_2$. Platinum-group metal (PGM) elements such as Pt, Pd, etc. and their derivatives show excellent electrocatalytic…
Active, selective and stable catalysts are imperative for sustainable energy conversion, and engineering materials with such properties are highly desired. High-entropy alloys (HEAs) offer a vast compositional space for tuning such…
Spin states are pivotal in modulating the electrocatalytic activity of transition-metal (TM)-based compounds, yet quantitatively evaluating the activity-spin state correlation remains a formidable challenge. Here, we propose an 'activity…
Transition-state searches are central to understanding reaction mechanisms, but the high computational cost of density-functional theory (DFT) limits their application in high-throughput catalyst and materials discovery. Machine-learned…
In this study, we present a sophisticated hybrid machine-learning framework that significantly improves the accuracy of predicting hydrogen storage capacities in metal hydrides. This is a critical challenge due to the scarcity of…
The development of noble-metal free electrocatalysts is of high importance for clean energy conversion applications. MoS$_2$ has been considered as a promising low cost catalyst for the hydrogen evolution reaction (HER), however its…
The rational design of transition metal dichalcogenide electrocatalysts for efficiently catalyzing hydrogen evolution reaction (HER) is believed to lead to the generation of a renewable energy carrier. To this end our work has made three…
High-entropy alloys (HEAs) and their two-dimensional counterparts (2D-HEAs) have recently attracted attention due to their tunable properties and catalytic potential, yet their chemical complexity makes direct density functional theory…
The accurate and efficient modeling of nuclear reactor transients is crucial for ensuring safe and optimal reactor operation. Traditional physics-based models, while valuable, can be computationally intensive and may not fully capture the…
Solid-state electrolytes are essential in the development of all-solid-state batteries. While density functional theory (DFT)-based nudged elastic band (NEB) and ab initio molecular dynamics (AIMD) methods provide fundamental insights on…
Artificial neural network potentials (NNPs) have emerged as effective tools for understanding atomic interactions at the atomic scale in various phenomena. Recently, we developed highly transferable NNPs for {\alpha}-iron and…
The energetics of hydrogen absorption in C15 cubic and C14 hexagonal TiCr$_2$H$_x$ Laves phases is investigated for $0 < x \le 6$ with density functional theory (DFT) and machine learning interatomic potentials (MLIPs). The MLIPs are…
Machine learning has emerged as a powerful tool for predicting molecular properties in chemical reaction networks with reduced computational cost. However, accurately predicting energies of transition state (TS) structures remains a…
Determining transition states (TSs) of surface reactions is central to understanding and designing heterogeneous catalysts but remains computationally prohibitive with density functional theory (DFT). While machine learning potentials…
The realization of novel heterostructures arising from the combination of nanomaterials is an effective way to modify their physicochemical and electrocatalytic properties, giving them enhanced characteristics stemming from their individual…
Computational virtual high-throughput screening (VHTS) with density functional theory (DFT) and machine-learning (ML)-acceleration is essential in rapid materials discovery. By necessity, efficient DFT-based workflows are carried out with a…