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We present time-dependent density functional theory (TDDFT) calculations of fluorescence emission energies for 284 distinct graphene quantum dots (GQDs) of varying shapes (square, hexagonal, and amorphous) and sizes ($\sim$1-2 nm). These…
We designed solution-processed, flexible hybrid graphene flake/2H-MoS2 quantum dot (QD) heterostructures, showing enhanced electrocatalytic activity for the hydrogen evolution reaction (HER) with respect to their native individual…
Two types of approaches to modeling molecular systems have demonstrated high practical efficiency. Density functional theory (DFT), the most widely used quantum chemical method, is a physical approach predicting energies and electron…
In the expanding field of generative artificial intelligence, integrating robust watermarking technologies is essential to protect intellectual property and maintain content authenticity. Traditionally, watermarking techniques have been…
In drug discovery, aqueous solubility is an important pharmacokinetic property which affects absorption and assay availability of drug. Thus, in silico prediction of solubility has been studied for its utility in virtual screening and lead…
The graph neural network (GNN) has been a powerful deep-learning tool in chemistry domain, due to its close connection with molecular graphs. Most GNN models collect and update atom and molecule features from the fed atom (and, in some…
Solvothermal synthesis of MoS2 based quantum dots (QDs) and the performance evaluation of bare QDs for the detection of aqueous As (III) oxidative state at room temperature and neutral pH over an extremely wide range (0.1 ppb to 1000 ppb)…
Most of the performances of electrochemical devices are governed by molecular processes taking place at the solution-electrode interfaces and molecular simulation are the main way to study these processes. Aqueous electrochemical systems…
Density functional theory (DFT) can be quite advantageous in advancing the field of catalysis because of the microscopic insights it provides, and thus can guide experimental searches of novel catalysts. Several recent works have…
Understanding strongly correlated systems is essential for advancing quantum chemistry and materials science, yet conventional methods like Density Functional Theory (DFT) often fail to capture their complex electronic behavior. To address…
Graphene functionalized with catalytic transition metals offers high-performance gas sensing by coupling graphene's exceptional electronic transport properties with the metal's catalytic activity, yet the atomistic relationships connecting…
Quantum Computing (QC) offers outstanding potential for molecular characterization and drug discovery, particularly in solving complex properties like the Ground State Energy (GSE) of biomolecules. However, QC faces challenges due to…
Machine Learning (ML) has become a promising tool for improving the quality of atomistic simulations. Using formaldehyde as a benchmark system for intramolecular interactions, a comparative assessment of ML models based on state-of-the-art…
Photocatalytic water splitting has emerged as a sustainable pathway for hydrogen production, leveraging sunlight to drive chemical reactions. This review explores the integration of density functional theory (DFT) with machine learning (ML)…
Synthesis of advanced inorganic materials with minimum number of trials is of paramount importance towards the acceleration of inorganic materials development. The enormous complexity involved in existing multi-variable synthesis methods…
In a world burdened by air pollution, the integration of state-of-the-art sensor calibration techniques utilizing Quantum Computing (QC) and Machine Learning (ML) holds promise for enhancing the accuracy and efficiency of air quality…
Electron density $\rho(\vec{r})$ is the fundamental variable in the calculation of ground state energy with density functional theory (DFT). Beyond total energy, features and changes in $\rho(\vec{r})$ distributions are often used to…
We investigate the impact of choosing regressors and molecular representations for the construction of fast machine learning (ML) models of thirteen electronic ground-state properties of organic molecules. The performance of each…
Graphene, a 2-dimensional monolayer form of sp2 hybridizated carbon atoms, is attracting increasing attention due to its unique and superior physicochemical properties. Covalently functionalized graphene layers, with their modifiable…
We present a machine-learning guided approach to predict saturation magnetization (MS) and coercivity (HC) in Fe-rich soft magnetic alloys, particularly Fe-Si-B systems. ML models trained on experimental data reveals that increasing Si and…