相关论文: High-Throughput Computational-Experimental Screeni…

Modern drug discovery is often time-consuming, complex and cost-ineffective due to the large volume of molecular data and complicated molecular properties. Recently, machine learning algorithms have shown promising results in virtual…

The design of efficient and cost-effective catalysts for the hydrogen evolution reaction (HER) is the key for molecular hydrogen (H2) production from electrochemical water splitting. Transition metal dichalcogenides (MX2), most notably…

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…

Two-dimensional transition metal dichalcogenides (TMDCs) have been extensively investigated due to their tunable properties. In this work, density functional theory (DFT) is employed to investigate the adsorption behavior and sensing…

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…

Machine learning is ideally suited for the pattern detection in large uniform datasets, but consistent experimental datasets on catalyst studies are often small. Here we demonstrate how a combination of machine learning and first-principles…

Developing a universal and precise design framework is crucial to search high-performance catalysts, but it remains a giant challenge due to the diverse structures and sites across various types of catalysts. To address this challenge,…

Two-dimensional (2D) materials, such as transition metal dichalcogenides (TMDCs) in the 2H or 1T crystal phases, are promising (electro)catalyst candidates due to their high surface to volume ratio and composition of low-cost, abundant…

2D semiconductors offer a promising pathway to replace silicon in next-generation electronics. Among their many advantages, 2D materials possess atomically-sharp surfaces and enable scaling the channel thickness down to the monolayer limit.…

Real-time PC based algorithm is developed for DSSSD ( Double Side Silicon Strip Detector) detector. Brief description of the detection system is also presented. Complete fusion nuclear reaction natYb+48Ca->217Th is used to test this…

A finite electronic band gap is a standard filter in high-throughput screening of materials using density functional theory (DFT). However, because of the systematic underestimation of band gaps in standard DFT approximations, a number of…

Among transition metal dichalcogenides (TMdCs) as alternatives for Pt-based catalysts, metallic-TMdCs catalysts have highly reactive basal-plane but are unstable. Meanwhile, chemically stable semiconducting-TMdCs show limiting catalytic…

Despite major advances in oncology, many chemotherapeutic agents still cause severe side effects that reduce quality of life, motivating new approaches for early detection and targeted elimination of cancer cells. Luminescent transition…

Two-dimensional (2D) monolayer pristine MoS$_2$ transition metal dichalcogenide (TMD) is the most studied material because of its promising aspects as nonprecious electrocatalyst for hydrogen evolution reaction (HER). Previous studies have…

The development of machine learning interatomic potentials faces a critical computational bottleneck with the generation and labeling of useful training datasets. We present a novel application of determinantal point processes (DPPs) to the…

We develop a DFT screening procedure for copper-substituted lead apatites of the composition Pb$_9$Cu(XO$_4$)$_6$Y that enforces three design rules: thermodynamic stability, Cu site preference, and symmetry robustness of the near-Fermi…

Developing novel protocols for hydrogen (H) loading is crucial for furthering the investigation of hydrides as potential high-temperature superconductors at lower pressures compared to recent discoveries. Ionic gating-induced protonation…

We develop a computational database, web-apps, and machine-learning (ML) models to accelerate the design and discovery of two-dimensional (2D)-heterostructures. Using density functional theory (DFT) based lattice-parameters and electronic…

A fundamental understanding of plasma-catalyst interactions is important for understanding reaction mechanisms, optimizing the catalyst, and increasing the efficiency of plasma-assisted catalytic process for ammonia (\ce{NH3}) synthesis. We…

Synthesis is a major challenge in the discovery of new inorganic materials. Currently, there is limited theoretical guidance for identifying optimal solid-state synthesis procedures. We introduce two selectivity metrics, primary and…