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Tognetti, Vincent; Joubert, Laurent
doi: 10.1002/jcc.27431pmid: 38847367
In this proof‐of‐concept paper, we show how exchange‐correlation effects can be simply recovered for interatomic energies within the interacting quantum atoms decomposition when local, gradient generalized, or meta‐gradient generalized approximations are used in density functional theory (DFT) calculations. We also demonstrate how inhomogeneity and non‐local effects can be introduced even from a pure local scheme, without resorting to any orbital information. Finally, we provide numerical evidence on a database of selected energetic molecules that this decomposition scheme can be efficiently used to build accurate models for the prediction of molecular energies from an initial “cheap” DFT calculation.
Denjean, Aurore E. F.; Rio, Jordan; Ciofini, Ilaria; Perrin, Marie‐Eve L.; Payard, Pierre‐Adrien
doi: 10.1002/jcc.27436pmid: 38847601
Mechanistic investigations at the density functional theory level of organic and organometallic reactions in solution are now broadly accessible and routinely implemented to complement experimental investigations. The selection of an appropriate functional among the plethora of developed ones is the first challenge on the way to reliable energy barrier calculations. To provide guidelines for the choice of an initial and reliable computational level, the performances of commonly used non‐empirical (PBE, PBE0, PBE0‐DH) and empirical density functionals (BLYP, B3LYP, B2PLYP) were evaluated relative to experimental activation enthalpies. Most reactivity databases to assess density functional performances are primarily based on high level calculations, here a set of experimental activation enthalpies of organic and organometallic reactions performed in solution were selected from the literature. As a general trend, the non‐empirical functionals outperform the empirical ones. The most accurate energy barriers are obtained with hybrid PBE0 and double‐hybrid PBE0‐DH density functionals, both providing similar performance. Regardless of the functional under consideration, the addition of the GD3‐BJ empirical dispersion correction does not enhance the accuracy of computed energy barriers.
doi: 10.1002/jcc.27374pmid: 38850166
Here, TS‐tools is presented, a Python package facilitating the automated localization of transition states (TS) based on a textual reaction SMILES input. TS searches can either be performed at xTB or DFT level of theory, with the former yielding guesses at marginal computational cost, and the latter directly yielding accurate structures at greater expense. On a benchmarking dataset of mono‐ and bimolecular reactions, TS‐tools reaches an excellent success rate of 95% already at xTB level of theory. For tri‐ and multimolecular reaction pathways ‐ which are typically not benchmarked when developing new automated TS search approaches, yet are relevant for various types of reactivity, cf. solvent‐ and autocatalysis and enzymatic reactivity ‐ TS‐tools retains its ability to identify TS geometries, though a DFT treatment becomes essential in many cases. Throughout the presented applications, a particular emphasis is placed on solvation‐induced mechanistic changes, another issue that received limited attention in the automated TS search literature so far.
Wang, Kai; Zhao, Jun; Guo, Junji; Chen, Shanbao; Zhao, Yapeng; Chen, Jiaye; Wang, Yarui; Liu, Le; Wang, Chaoyong; Liu, Zhiqing
doi: 10.1002/jcc.27448pmid: 38872590
Due to the potential applications in next‐generation micro/nano electronic devices and functional materials, magnetic germanium (Ge)‐based clusters are receiving increasing attention. In this work, we reported the structures, electronic and magnetic properties of CrMnGen with sizes n = 3–20. Transition metals (TMs) of Cr and Mn tend to stay together and be surrounded by Ge atoms. Small sized clusters with n ≤ 8 prefer to adopt bipyramid‐based structures as the motifs with the excess Ge atoms absorbed on the surface. Starting from n = 9, the structure with one TM atom interior appears and persists until n = 16, and for larger sizes n = 17–20, the two TM atoms are full‐encapsulated by Ge atoms to form endohedral structures. The Hirshfeld population analyses show that Cr atom always acts as the electron donor, while Mn atom is always the acceptor except for sizes 3 and 6. The average binding energies of these clusters increase with cluster size n, sharing a very similar trend as that of CrMnSin (n = 4–20) clusters, which indicates that it is favorable to form large‐sized clusters. CrMnGen (n = 6, 13, 16, 19, and 20) clusters prefer to exhibit ferromagnetic Cr–Mn coupling, while the remaining clusters are ferrimagnetic.
Chaquin, Patrick; Fuster, Franck; Markovits, Alexis
doi: 10.1002/jcc.27447pmid: 38887140
Observational data show complex organic molecules in the interstellar medium (ISM). Hydrogenation of small unsaturated carbon double bond could be one way for molecular complexification. It is important to understand how such reactivity occurs in the very cold and low‐pressure ISM. Yet, there is water ice in the ISM, either as grain or as mantle around grains. Therefore, the addition of atomic hydrogen on double‐bonded carbon in a series of seven molecules have been studied and it was found that water catalyzes this reaction. The origin of the catalysis is a weak charge transfer between the π MO of the unsaturated molecule and H atom, allowing a stabilizing interaction with H2O. This mechanism is rationalized using the non‐covalent interaction and the quantum theory of atoms in molecules approaches.
Azevedo, Walter Filgueira; Quiroga, Rodrigo; Villarreal, Marcos Ariel; Silveira, Nelson José Freitas; Bitencourt‐Ferreira, Gabriela; Silva, Amauri Duarte; Veit‐Acosta, Martina; Oliveira, Patricia Rufino; Tutone, Marco; Biziukova, Nadezhda; Poroikov, Vladimir; Tarasova, Olga; Baud, Stéphaine
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Herein, we present a density functional theory with dispersion correction (DFT‐D) calculations that focus on the intercalation of ionic liquids (ILs) electrolytes into the two‐dimensional (2D) Ti3C2Tx MXenes. These ILs include the cation 1‐ethyl‐3‐methylimidazolium (Emim+), accompanied by three distinct anions: bis(trifluoromethylsulfonyl)imide (TFSA−), (fluorosulfonyl)imide (FSA−) and fluorosulfonyl(trifluoromethanesulfonyl)imide (FTFSA−). By altering the surface termination elements, we explore the intricate geometries of IL intercalation in neutral, negative, and positive pore systems. Accurate estimation of charge transfer is achieved through five population analysis models, such as Hirshfeld, Hirshfeld‐I, DDEC6 (density derived electrostatic and chemical), Bader, and VDD (voronoi deformation density) charges. In this work, we recommend the DDEC6 and Hirshfeld‐I charge models, as they offer moderate values and exhibit reasonable trends. The investigation, aimed at visualizing non‐covalent interactions, elucidates the role of cation‐MXene and anion‐MXene interactions in governing the intercalation phenomenon of ionic liquids within MXenes. The magnitude of this role depends on two factors: the specific arrangement of the cation, and the nature of the anionic species involved in the process.
doi: 10.1002/jcc.27449pmid: 38900052
Classical scoring functions may exhibit low accuracy in determining ligand binding affinity for proteins. The availability of both protein–ligand structures and affinity data make it possible to develop machine‐learning models focused on specific protein systems with superior predictive performance. Here, we report a new methodology named SAnDReS that combines AutoDock Vina 1.2 with 54 regression methods available in Scikit‐Learn to calculate binding affinity based on protein–ligand structures. This approach allows exploration of the scoring function space. SAnDReS generates machine‐learning models based on crystal, docked, and AlphaFold‐generated structures. As a proof of concept, we examine the performance of SAnDReS‐generated models in three case studies. For all three cases, our models outperformed classical scoring functions. Also, SAnDReS‐generated models showed predictive performance close to or better than other machine‐learning models such as KDEEP, CSM‐lig, and ΔVinaRF20. SAnDReS 2.0 is available to download at https://github.com/azevedolab/sandres.