Automated and Efficient Generation of General Molecular Aggregate Structures

Metal-Organic Cage Receptors for Encapsulation and Sensing of Bile Acids

Developing synthetic supramolecular receptors to solubilize, scavenge, recognize, encapsulate, and sense steroids is challenging. Despite a limited number of receptors having affinity with steroids, none exists to bind steroidal bile acids selectively. Herein, we report a C2-symmetric metal-organic cage [Pd6L24]12+ and an expanded version of the Fujita cage [Pd6L14]12+, built with a conformationally flexible ligand L2, accessed through coordination-driven self-assembly. We examined both cages for steroid recognition in water: both have certain shared characteristics and distinctive features. [Pd6L14]12+ binds hydrophobic bile acids and other steroids by forming a 1:1 complex. In contrast, the expanded [Pd6L24]12+ cage exhibits an affinity for amphiphilic bile acids and selective steroids to encapsulate them as dimers, promoted by cooperative interguest hydrogen bonding. [Pd6L24]12+ has a 5 times stronger solubility enhancement ability for cholic acid compared to [Pd6L14]12+. Further, the expanded [Pd6L24]12+ cage can selectively sense bile acids in nanomolar detection limits through indicator displacement assay by employing sulforhodamine 101 (SR101).

Conformational energies of biomolecules in solution: Extending the MPCONF196 benchmark with explicit water molecules

A prerequisite for the computational prediction of molecular properties like conformational energies of biomolecules is a reliable, robust, and computationally affordable method usually selected according to its performance for relevant benchmark sets. However, most of these sets comprise molecules in the gas phase and do not cover interactions with a solvent, even though biomolecules typically occur in aqueous solution. To address this issue, we introduce a with explicit water molecules solvated version of a gas-phase benchmark set containing 196 conformers of 13 peptides and other relevant macrocycles, namely MPCONF196 [J. Řezáč et al., JCTC 2018, 14, 1254-1266], and provide very accurate PNO-LCCSD(T)-F12b/AVQZ' reference values. The novel solvMPCONF196 benchmark set features two additional challenges beyond the description of conformers in the gas phase: conformer-water and water-water interactions. The overall best performing method for this set is the double hybrid revDSDPBEP86-D4/def2-QZVPP yielding conformational energies of almost coupled cluster quality. Furthermore, some (meta-)GGAs and hybrid functionals like B97M-V and ω B97M-D with a large basis set reproduce the coupled cluster reference with an MAD below 1 kcal mol- 1  . If more efficient methods are required, the composite DFT-method r2 SCAN-3c (MAD of 1.2 kcal mol- 1 ) is a good alternative, and when conformational energies of polypeptides or macrocycles with more than 500-1000 atoms are in the focus, the semi-empirical GFN2-xTB or the MMFF94 force field (for very large systems) are recommended.

A comparative study of ammonia solubility in imidazolium-based ionic liquids with different structural compositions

Four imidazolium-based ionic liquids (ILs) with two cations 1-pentyl-3-butylimidazolium [PBIM]+ and 1-benzyl-3-butylimidazolium tetrafluoroborate [BzBIM]+, and two anions tetrafluoroborate (BF4-) and trifluoromethanesulfonate (OTf-) were synthesized for NH3 solubility enhancement. The structural, thermal, and electrochemical stabilities, ionic conductivity, and viscosity of the four ILs, namely, [PBIM]BF4, [BzBIM]BF4, [PBIM]OTf, and [BzBIM]OTf, were investigated. Due to the intermolecular interaction of the benzyl group attached to the imidazolium ring, [BzBIM]+-based ILs exhibited higher thermal stability but lower ionic conductivity compared to [PBIM]+-based ILs. Further, the NH3 solubility in all ILs was measured using a custom-made setup at temperatures ranging from 293.15 to 323.15 K and pressures ranging from 1 to 5 bar. The effects of the cation and anion structures of ILs, as well as pressure and temperature, on the NH3 solubility in the ILs were also investigated. [PBIM]BF4 showed the best solubility because of its high free volume and low viscosity. Density functional calculations validated the superior NH3 solubility in [PBIM]BF4, attributable to the minimal reorganization of the [cation]anion complex geometry during the solvation process, yielding a low solvation free energy. The findings of this study suggest that ILs exhibit a high NH3 solubility capacity and cation and anion structures considerably affect the NH3 solubility in ILs.

ATRP catalysts of tetradentate guanidine ligands - do guanidine donors induce a faster atom transfer?

Tripodal tetradentate N donor ligands stabilise the most active ATRP catalyst systems. Here, we set out to synthesise the new guanidine ligand TMG-4NMe2uns-penp, inspired by p-substituted tris(2-pyridylmethyl)amine (TPMA) ligands. The impact of changing pyridine against guanidine donors was examined through solid state and solution experiments and density functional theory (DFT) calculations. In the solid state, the molecular structures of copper complexes based on the ligands TMG-4NMe2uns-penp, TMG-uns-penp and TMG3tren were discussed concerning the influence of a NMe2 substituent at the pyridines and the guanidine donors. In solution, the TMG-4NMe2uns-penp system was investigated by several methods, including UV/Vis, EPR and NMR spectroscopy indicating similar properties to that of the highly active TPMANMe2 system. The redox potentials were determined and related to the catalytic activity. Besides the expected trends between these and the ligand structures, there is evidence that guanidine donors in tripodal ligand systems lead to a better deactivation and possibly a faster exchange within the ATRP equilibrium than TPMA systems. Supported by DFT calculations, it derives from an easier cleavable Cu-Br bond of the copper(II) deactivator species. The high activity was stated by a controlled initiator for continuous activator regeneration (ICAR) ATRP of styrene.

Complexes of resorcin[4]arene with secondary amines: synthesis, solvent influence on "in-out" structure, and theoretical calculations of non-covalent interactions

Resorcin[4]arenes (R[4]A) are macrocyclic compounds with a cavity structure. Despite a relatively small cavity, these compounds are capable of forming complexes with small organic molecules. The current paper focuses on the synthesis of complexes between R[4]A and secondary aliphatic amines (sec-amines). Through NMR spectroscopy, it was observed that "in-out" complexes are formed depending on the solvent. It was also found that the stoichiometry of the formed complexes depends on the size of the amine molecule. The automated interaction sites screening (aISS) made it possible to generate molecular ensembles of complexes. The geometry of the ensembles was first optimized with the r2scan-3c functional and, finally, the structure with the lowest energy, with the functional PBE0-D4/mTZVPP/CPCM. The Hartree-Fock plus London dispersion (HFLD) method was used for the study of non-covalent interactions (NCI). The calculations lead to the conclusion that a reduction in electrostatic interactions and an increase in exchange and dispersion interactions in CHCl3 in relation to DMSO are the driving forces behind the placement of sec-amine molecules into the R[4]A cavity and the formation of "in" type complexes.

Möbius carbon nanobelts interacting with heavy metal nanoclusters

CONTEXT

The interaction between carbon nanostructures and heavy metal clusters is of great interest due to their potential applications as sensors and filters to remove the former from environment. In this work, we investigated the interaction between two types of carbon nanobelts (Möbius-type nanobelt and simple nanobelt) and nickel, cadmium, and lead nanoclusters. Our aim was to determine how both systems interact which would shed light on the potential applications of the carbon nanostructures as pollutant removal and detecting devices.

METHODS

To investigate the interaction between carbon nanostructures and heavy metal nanoclusters, we utilized the semiempirical tight binding framework provided by xTB software with the GFN2-xTB Hamiltonian. We performed calculations to determine the best interaction site, lowest energy geometries, complexes stability (using molecular dynamics at 298K), binding energy, and electronic properties. We also carried out a topological study to investigate the nature and intensity of the bonds formed between the metal nanoclusters and the nanobelts. Our results demonstrate that heavy metal nanoclusters have a favorable binding affinity towards both nanobelts, with the Möbius-type nanobelt having a stronger interaction. Additionally, our calculations reveal that the nickel nanocluster has the lowest binding energy, displaying the greatest charge transfer with the nanobelts, which was nearly twice that of the cadmium and lead nanoclusters. Our combined results lead to the conclusion that the nickel nanoclusters are chemisorbed, whereas cadmium and lead nanoclusters are physisorbed in both nanobelts. These findings have significant implications for the development of sensor and filtering devices based on carbon and heavy metal nanoclusters.

Machine Learning for Bridging the Gap between Density Functional Theory and Coupled Cluster Energies

Accurate electronic energies and properties are crucial for successful reaction design and mechanistic investigations. Computing energies and properties of molecular structures has proven extremely useful, and, with increasing computational power, the limits of high-level approaches (such as coupled cluster theory) are expanding to ever larger systems. However, because scaling is highly unfavorable, these methods are still not universally applicable to larger systems. To address the need for fast and accurate electronic energies of larger systems, we created a database of around 8000 small organic monomers (2000 dimers) optimized at the B3LYP-D3(BJ)/cc-pVTZ level of theory. This database also includes single-point energies computed at various levels of theory, including PBE1PBE, ωΒ97Χ, M06-2X, revTPSS, B3LYP, and BP86, for density functional theory as well as DLPNO-CCSD(T) and CCSD(T) for coupled cluster theory, all in conjunction with a cc-pVTZ basis. We used this database to train machine learning models based on graph neural networks using two different graph representations. Our models are able to make energy predictions from B3LYP-D3(BJ)/cc-pVTZ inputs to CCSD(T)/cc-pVTZ outputs with a mean absolute error of 0.78 and to DLPNO-CCSD(T)/cc-pVTZ with an mean absolute error of 0.50 and 0.18 kcal mol^-1 for monomers and dimers, respectively. The model for dimers was further validated on the S22 database, and the monomer model was tested on challenging systems, including those with highly conjugated or functionally complex molecules.

ONIOM meets : efficient, accurate, and robust multi-layer simulations across the periodic table

The computational treatment of large molecular structures is of increasing interest in fields of modern chemistry. Accordingly, efficient quantum chemical approaches are needed to perform sophisticated investigations on such systems. This engaged the development of the well-established "Our own N-layered integrated molecular orbital and molecular mechanics" (ONIOM) multi-layer scheme [L. W. Chung et al., Chem. Rev., 2015, 115, 5678-5796]. In this work, we present the specific implementation of the ONIOM scheme into the xtb semi-empirical extended tight-binding program package and its application to challenging transition-metal complexes. The efficient and broadly applicable GFNn-xTB and -FF methods are applied in the ONIOM framework to elucidate reaction energies, geometry optimizations, and explicit solvation effects for metal-organic systems with up to several hundreds of atoms. It is shown that an ONIOM-based combination of density functional theory, semi-empirical, and force-field methods can be used to drastically reduce the computational costs and thus enable the investigation of huge systems at almost no significant loss in accuracy.