Recent Advancements in Density Functional Theory (DFT) and beyond for Computational Chemistry

Dear Colleagues,

Density functional theory (DFT) has shown unsurpassed influence in computational chemistry in terms of its performance, compared to wave-function-based electron correlation methods. DFT constitutes an outset for dynamical mean field theory, many-body perturbation theory, density functional perturbation theory, and so forth. Through quantum mechanics, DFT has enabled researchers to evaluate characteristics of molecules, bulk crystals, biological matter and nanodevices. Consequently, DFT has risen to prominence in computational, as well as theoretical, chemistry. However, narrow computational intricacy leads to limited DFT applications. Hence, developing new accelerating computational algorithms to obtain coherent results for complex systems at a feasible computational price is imperative. Linear scaling techniques describe an array of such strategies where the cost of computation corresponds to the size of the structure. At present, technical and fundamental research surrounds excitations in solids and molecules by employing theoretical methods. Further, DFT-based analysis has been one of the most basic and important strategies for drug discovery, allowing the prediction of molecular interactions that hold together a protein and a ligand in the bound state.

The present Special Issue aims to examine new techniques such as combining computational chemistry and machine learning methods, mechanistic study, chemosensor behavior, photovoltaic and opto-electronic properties (NLO and solar cells), to obtain insightful information from DFT methods that are applicable to molecules. We welcome research articles beyond the traditional areas of DFT study, such as applying machine learning to the design and analysis of new materials.

Dr. Muhammad Khalid
Dr. Muhammad Nadeem Arshad
Guest Editors

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