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The Fukui function is named after Kenichi Fukui, who investigated the frontier orbitals described by the function, specifically the HOMO and LUMO. [3] Fukui functions are related in part to the frontier molecular orbital theory (also known as the Fukui theory of reactivity and selection, also developed by Kenichi Fukui) which discusses how nucleophiles attack the HOMO while at the same time ...
Yang's main contributions to theoretical chemistry range from fundamental theory to applications of density functional theory. He (with Parr) developed the concepts of the Fukui function, [1] hardness, and softness [2] in density functional theory. He also justified the theoretical ground of potential functional (as in Optimized-Effective ...
Year Name Authors References Language Short Description 1983 BHMIE [3]: Craig F. Bohren and Donald R. Huffman [1]Fortran IDL Matlab C Python "Mie solutions" (infinite series) to scattering, absorption and phase function of electromagnetic waves by a homogeneous sphere.
In 1952, Kenichi Fukui published a paper in the Journal of Chemical Physics titled "A molecular theory of reactivity in aromatic hydrocarbons." [1] Though widely criticized at the time, he later shared the Nobel Prize in Chemistry with Roald Hoffmann for his work on reaction mechanisms.
The test functions used to evaluate the algorithms for MOP were taken from Deb, [4] Binh et al. [5] and Binh. [6] The software developed by Deb can be downloaded, [ 7 ] which implements the NSGA-II procedure with GAs, or the program posted on Internet, [ 8 ] which implements the NSGA-II procedure with ES.
The first family of Minnesota functionals, published in 2005, is composed by: M05: [13] Global hybrid functional with 28% HF exchange. M05-2X [14] Global hybrid functional with 56% HF exchange.
Physics-informed neural networks for solving Navier–Stokes equations. Physics-informed neural networks (PINNs), [1] also referred to as Theory-Trained Neural Networks (TTNs), [2] are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations (PDEs).
DMol 3 is a commercial (and academic) software package which uses density functional theory with a numerical radial function [1] basis set to calculate the electronic properties of molecules, clusters, surfaces and crystalline solid materials [2] from first principles.