Related papers: High-throughput computational characterization of …
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…
Transition metal dichalcogenides (TMD) are currently among the most interesting two-dimensional (2D) materials due to their outstanding properties. MoTe2 involves attractive polymorphic TMD crystals which can exist in three different 2D…
Large-scale density functional theory (DFT) calculations provide a powerful tool to investigate the atomic and electronic structure of materials with complex structures. This article reviews a large-scale DFT calculation method, the…
Linear-response quantum electrodynamical density functional theory (QEDFT) enables the description of molecular spectra under strong coupling to quantized photonic modes, such as those in optical cavities. Recently, this approach was…
Deep learning models are widely used for the data-driven design of materials based on atomic force microscopy (AFM) and other scanning probe microscopy. These tools enhance efficiency in inverse design and characterization of materials.…
The discovery of archetypal two-dimensional (2D) materials provides enormous opportunities in both fundamental breakthroughs and device applications, as evident by the research booming in graphene, atomically thin transition-metal…
Layered metal dichalcogenide materials are a family of semiconductors with a wide range of energy band gaps and properties, and potential to open up new areas of physics and technology applications. However, obtaining high crystal quality…
In transition metal dichalcogenides (TMDCs), charge carriers have spin, pseudospin, and valley degrees of freedom associated with magnetic moments. The monolayers and bilayers of the TMDCs, in particular, MoS$_2$, lead strong couplings…
Two-dimensional (2D) transition metal dichalcogenides (TMDCs), exemplified by molybdenum disulfide (MoS2), have shown exceptional potential for data-centred, energy-efficient electronic applications due to their unique electrical,…
We explore a material design strategy to optimize the thermoelectric power factor. The approach is based on screening the band structure changes upon a controlled volume change. The methodology is applied to the binary silicides and…
Approximate density functional theory (DFT) has become indispensable owing to its cost-accuracy trade-off in comparison to more computationally demanding but accurate correlated wavefunction theory. To date, however, no single density…
The fundamental quantity governing the mechanical and thermodynamic properties of a crystalline solid is its electronic charge density. Yet, its direct use for the rapid prediction of materials properties remains challenging due to its high…
We present a computationally efficient method to obtain the spectral function of bulk systems in the framework of steady-state density functional theory (i-DFT) using an idealized Scanning Tunneling Microscope (STM) setup. We calculate the…
We introduce the first systematic database of scanning tunneling microscope (STM) images obtained using density functional theory (DFT) for two-dimensional (2D) materials, calculated using the Tersoff-Hamann method. It currently contains…
We use density functional theory (DFT) with the generalized gradient approximation (GGA) and our first-principles extrapolation method for accurate chemisorption energies {[Mason {\em et al.}, Phys. Rev. B {\bf 69}, 161401R (2004)]} to…
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…
Machine learning (ML) models utilizing structure-based features provide an efficient means for accurate property predictions across diverse chemical spaces. However, obtaining equilibrium crystal structures typically requires expensive…
We present a numerical modeling workflow based on machine learning (ML) which reproduces the the total energies produced by Kohn-Sham density functional theory (DFT) at finite electronic temperature to within chemical accuracy at negligible…
Low-temperature plasma processing is a promising technique for tailoring transition metal dichalcogenides (TMDs). For chalcogen substitution processing, a key challenge is to identify the ion energy window that enables selective chalcogen…
The electronic band structures along with Fermi surface, anisotropy, Vickers hardness, analysis of Mulliken populations, optical and thermodynamic properties were studied for the first time. The optimized unit cell parameters are compared…