Gold(I)-Thiolate Coordination Polymers as Multifunctional Materials: The Case of Au(I)-p-Fluorothiophenolate.

Gold-sulfur interaction has vital importance in nanotechnologies and material chemistry to design functional nanoparticles, self-assembled monolayers, or molecular complexes. In this paper, a mixture of only two basic precursors, such as the chloroauric acid (HAu(III)Cl4) and a thiol molecule (p-fluorothiophenol (p-HSPhF)), are used for the synthesis of gold(I)-thiolate coordination polymers. Under different conditions of synthesis and external stimuli, five different functional materials with different states of [Au(I)(p-SPhF)]n can be afforded. These gold-thiolate compounds are (i) red emissive, flexible, and crystalline fibers; (ii) composite materials made of these red emissive fibers and gold nanoparticles; (iii) amorphous phase; (iv) transparent glass; and (v) amorphous-to-crystalline phase-change material associated with an ON/OFF switch of luminescence. The different functionalities of these materials highlight the great versatility of the gold(I) thiolate coordination polymers with easy synthesis and diverse shaping that may have great potential as sustainable phosphors, smart textiles, sensors, and phase change memories.

Threading Subunits for Polymers to Predict the Equilibrium Ensemble of Solid Polymer Electrolytes.

We present a computational method for polymer growth called "threading subunits for polymers (TSP)" that can efficiently sample solid polymer electrolyte structures with extended conformations. The TSP method involves equilibrating subunit (e.g., monomer) conformations that form favorable solvation ion shells, followed by consecutively connecting the subunits and minimizing the structures. The TSP method can sample polymers with good solvent-like conformations and from near-equilibrium structures in which ions are well-dispersed, avoiding unusual ion clustering under ambient conditions. Using the TSP method, the equilibration time can be reduced significantly by effectively sampling the polymer conformations near equilibrium. We anticipate that the TSP method can be applied to simulate various polymer electrolytes.

MolNet_Equi: A Chemically Intuitive, Rotation-Equivariant Graph Neural Network.

Although deep-learning (DL) models suggest unprecedented prediction capabilities in tackling various chemical problems, their demonstrated tasks have so far been limited to the scalar properties including the magnitude of vectorial properties, such as molecular dipole moments. A rotation-equivariant MolNet_Equi model, proposed in this paper, understands and recognizes the molecular rotation in the 3D Euclidean space, and exhibits the ability to predict directional dipole moments in the rotation-sensitive mode, as well as showing superior performance for the prediction of scalar properties. Three consecutive operations of molecular rotation R M ${\left(R\left(M\right)\right)}$ , dipole-moment prediction φ µ R M ${\left({\phi{} }_{\mu }\left(R\left(M\right)\right)\right)}$ , and dipole-moment inverse-rotation R - 1 φ µ R M ${\left({R}^{-1}\left({\phi{} }_{\mu }\left(R\left(M\right)\right)\right)\right)}$ do not alter the original prediction of the total dipole moment of a molecule φ µ M ${\left({\phi{} }_{\mu }\right(M\left)\right)}$ , assuring the rotational equivariance of MolNet_Equi. Furthermore, MolNet_Equi faithfully predicts the absolute direction of dipole moments given molecular poses, albeit the model has been trained only with the information on dipole-moment magnitudes, not directions. This work highlights the potential of incorporating fundamental yet crucial chemical rules and concepts into DL models, leading to the development of chemically intuitive models.

Tuning the 1D-2D dimensionality upon ligand exchange in silver thiolate coordination polymers with photoemission switching.

Silver nanoparticles are known and widely used for their antimicrobial activities. Nevertheless, once they are released into the natural or biological environments, they can become toxic with time, because of the dissolution of some Ag(I) ions that can then react with thiol-based molecules, such as glutathione and/or compete with copper proteins. These assumptions are based on the high affinity of the soft acid Ag(I) and the soft base thiolates and the exchange reactions that are involved in complex physiological media. Here we synthesized and fully characterized two new 2D silver thiolate coordination polymers (CPs) that exhibit a reversible 2D-to-1D structural transformation in the presence of an excess of thiol molecules. This dimensionality change induces also a switch of the yellow emission of the Ag-thiolate CP. This study highlights that these highly stable silver-thiolate CPs, in basic, acidic and oxidant media can undergo a complete dissolution-recrystallization mechanism upon thiol exchange reactions.

Turn Off-On Fluorescent CO2 Gas Detection Based on Amine-Functionalized Imidazole-Based Poly(ionic liquid).

Poly(ionic liquids) (PILs) have been widely used for CO2 capture because their characteristics resemble those of an ionic liquid, yet they have properties typically associated with polymers. We studied the application of the amine-functionalized poly(vinylimidazole)-based PIL (PVIm-NH2) as a chemosensor. The PVIm-NH2 was successfully prepared by a facile and low-cost method and was characterized by several analytical techniques: proton nuclear magnetic resonance (1H NMR), Fourier transform infrared (FT-IR) spectroscopy, gel permeation chromatography (GPC), and spectrofluorometry. The ability of PVIm-NH2 to detect CO2 gas was evaluated in the presence of triethylamine (TEA). Under optimized conditions, the detection limit was calculated to be 2.86 × 10-3 M with R 2 = 0.9906. Moreover, theoretical and experimental studies suggested a plausible mechanism whereby PVIm-NH2 generates N-heterocyclic carbenes (NHCs) in the presence of TEA, which further reacts with CO2 gas in aqueous media to form a carboxylic acid. Analysis of PVIm-NH2 before and after the addition of TEA using the 1H NMR technique showed the disappearance of the proton peak, thus suggesting a successful generation of NHC. Further analysis via 13C NMR revealed the reaction of CO2 and NHC to form a carboxylic acid group. Finally, we demonstrated that PIL is a promising candidate as a chemosensor through diverse structural modifications.

MolNet: A Chemically Intuitive Graph Neural Network for Prediction of Molecular Properties.

Most graph neural networks (GNNs) in deep-learning chemistry collect and update atom and molecule features from the fed atom (and, in some cases, bond) features, basically based on the two-dimensional (2D) graph representation of 3D molecules. However, the 2D-based models do not faithfully represent 3D molecules and their physicochemical properties, exemplified by the overlooked field effect that is a "through-space" effect, not a "through-bond" effect. We propose a GNN model, denoted as MolNet, which accommodates the 3D non-bond information in a molecule, via a noncovalent adjacency matrix A ‾ , and also bond-strength information from a weighted bond matrix B . Comparative studies show that MolNet outperforms various baseline GNN models and gives a state-of-the-art performance in the classification task of BACE dataset and regression task of ESOL dataset. This work suggests a future direction for the construction of deep-learning models that are chemically intuitive and compatible with the existing chemistry concepts and tools.

Rotational Variance-Based Data Augmentation in 3D Graph Convolutional Network.

This work proposes the data augmentation by molecular rotation, with consideration that the protein-ligand binding events are rotation-variant. As a proof-of-concept, known active (i. e., 1-labeled) ligands to human ß-secretase 1 (BACE-1) are rotated for the generation of 0-labeled data, and the rotation-dependent prediction accuracy of 3D graph convolutional network (3DGCN) is investigated after data augmentation. The data augmentation makes the orientation-recognizing ability of 3DGCN improved significantly in the classification task for BACE-1/ligand binding. Furthermore, the data-augmented 3DGCN has a capability for predicting active ligands from a candidate dataset, via improved performance of orientation recognition, which would be applied to virtual drug screening and discovery.

Assessment and prediction of band edge locations of nitrides using a self-consistent hybrid functional.

Due to their optimal bandgap size and large defect tolerance, nitrides are becoming pivotal materials in several optoelectronic devices, photovoltaics, and photocatalysts. A computational method that can accurately predict their electronic structures is indispensable for exploring new nitride materials. However, the relatively small bandgap of nitrides, which stems from the subtle balance between ionic and covalent bond characteristics, makes conventional density functional theory challenging to achieve satisfactory accuracy. Here, we employed a self-consistent hybrid functional where the Hartree-Fock mixing parameter is self-consistently determined and thus the empiricism of the hybrid functional is effectively removed to calculate the bandgaps of various nitride compounds. By comparing the bandgaps from the self-consistent hybrid functional calculations with the available experimental and high-level GW calculation results, we found that the self-consistent hybrid functional can provide a computationally efficient approach for quantitative predictions of nitride electronic structures with an accuracy level comparable to the GW method. Additionally, we aligned the band edge positions of various nitride compounds using self-consistent hybrid functional calculations, providing material design principles for heterostructures of nitride-based optoelectronic devices. We anticipate the wide use of the self-consistent hybrid functional for accelerating explorations and predictions of new nitride-based functional materials in various photoactive applications.

Adsorption mechanisms of fatty acids on fluorite unraveled by infrared spectroscopy and first-principles calculations.

HYPOTHESIS: The adsorption mechanisms of fatty acids on minerals are largely debated from years, and their understanding is now required to improve flotation processing in the critical context of raw materials. Three wavenumbers have been observed in the literature for the asymmetric stretching vibration of COO- after the adsorption of fatty acids on mineral surfaces. They have been interpreted as different adsorbed forms, such as a precipitate formation, an adsorption of sole or bridged carboxylates, an anion exchange, or adsorbed modes, such as monodentate or bidentate configurations. EXPERIMENTS/THEORY: Diffuse reflectance infrared Fourier transform spectroscopy was combined with ab initio molecular dynamics simulations and simulation of infrared spectra. Fluorite and sodium octanoate - or longer-chain fatty acids - were used as prototypical materials for all the investigations. FINDINGS: At low fatty acids concentration, the asymmetric stretching vibration of COO- peaks at 1560 cm-1 while, at higher concentration, this infrared band converts into a doublet peaking at 1535 and 1575 cm-1. Using simulations, we assign the band at 1560 cm-1 to the adsorption of a carboxylate molecule bridged on a sodium counter-cation and the doublet at 1535 and 1575 cm-1 to the adsorption of the sole carboxylate anion under a monodentate or a bidentate binuclear configuration, respectively. The formation of an adsorbed layer on the mineral surface is initiated by the adsorption of a sodium carboxylate and followed by the adsorption of mixed sole anionic forms. The role of the carboxylate counter-cation is highlighted for the first time, which was totally ignored in the literature beforehand. This particularly opens the path to the development of innovative strategies to enhance the separation contrast between minerals, which is of uttermost importance for the recovery of critical raw materials.

Lifshitz Transition and Non-Fermi Liquid Behavior in Highly Doped Semimetals.

The classical Fermi liquid theory and Drude model have provided fundamental ways to understand the resistivity of most metals. The violation of the classical theory, known as non-Fermi liquid (NFL) transport, appears in certain metals, including topological semimetals, but quantitative understanding of the NFL behavior has not yet been established. In particular, the determination of the non-quadratic temperature exponent in the resistivity, a sign of NFL behavior, remains a puzzling issue. Here, a physical model to quantitatively explain the Lifshitz transition and NFL behavior in highly doped (a carrier density of ≈1022 cm-3 ) monoclinic Nb2 Se3 is reported. Hall and magnetoresistance measurements, the two-band Drude model, and first-principles calculations demonstrate an apparent chemical potential shift by temperature in monoclinic Nb2 Se3 , which induces a Lifshitz transition and NFL behavior in the material. Accordingly, the non-quadratic temperature exponent in the resistivity can be quantitatively determined by the chemical potential shift under the framework of Fermi liquid theory. This model provides a new experimental insight for nontrivial transport with NFL behavior or sign inversion of Seebeck coefficients in emerging materials.

uMBD: A Materials-Ready Dispersion Correction That Uniformly Treats Metallic, Ionic, and van der Waals Bonding.

Materials design increasingly relies on first-principles calculations for screening important candidates and for understanding quantum mechanisms. Density functional theory (DFT) is by far the most popular first-principles approach due to its efficiency and accuracy. However, to accurately predict structures and thermodynamics, DFT must be paired with a van der Waals (vdW) dispersion correction. Therefore, such corrections have been the subject of intense scrutiny in recent years. Despite significant successes in organic molecules, no existing model can adequately cover the full range of common materials, from metals to ionic solids, hampering the applications of DFT for modern problems such as battery design. Here, we introduce a universally optimized vdW-corrected DFT method that demonstrates an unbiased reliability for predicting molecular, layered, ionic, metallic, and hybrid materials without incurring a large computational overhead. We use our method to accurately predict the intercalation potentials of layered electrode materials of a Li-ion battery system, a problem for which the existing state-of-the-art methods fail. Thus, we envisage broad use of our method in the design of chemo-physical processes of new materials.

Theoretical study of the microscopic origin of magnetocrystalline anisotropy in Fe16N2 and its alloys: comparison with the other L10 alloys.

We study the magnetocrystalline anisotropy (MCA) energy of Fe16-n X n N2 ([Formula: see text]), where X = Ti, V, Cr, Mn, Co, Ni, Cu. To understand the microscopic origin and basic mechanism controlling the MCA energy of Fe16-n X n N2, we first examined the behavior of the MCA energy of Fe16N2, focusing on the spin-orbit coupling (SOC), and compared the behavior with other alloy systems (FeCo, FePt and CoPt) with L10 structure. We find that whereas the MCA energy of FeCo is determined by the spin-conserved terms of the SOC energy, the MCA energy of Fe16N2 is determined by mutual competition between spin-conserved and spin-flip terms. We then studied the effect of the transition element X on the phase stability and MCA of Fe16-n X n N2. The MCA energy and cohesive energy are calculated to determine the most stable configuration for each choice of X and n, and compared with those of Fe16N2. For X = V and Cu, both the MCA and phase stability improved noticeably. For X = Co, the MCA energy improves, but Fe16-n Co n N2 is less stable than Fe16N2. The microscopic mechanism underlying the MCA energy enhancement due to X = V, Cu and Co in Fe16-n X n N2 was studied by examining the data for spin- and site-resolved projected density of states (PDOS), as well as each spin-conserved and spin-flip terms contributing to the SOC energy.

Benchmarking the performance of approximate van der Waals methods for the structural and energetic properties of SiO2 and AlPO4 frameworks.

Density functional theory (DFT) calculations using sixteen different approaches, fourteen of which were designed to include dispersion interactions [DFT + D and van der Waals (vdW)-DF methods], were performed for a set of sixteen framework compounds with either SiO2 or AlPO4 composition. The compounds include four dense structures (α-quartz, α-cristobalite, and their AlPO4 analogues), eight all-silica zeolites, and four aluminophosphate zeotypes (AlPOs). We analyzed the performance in reproducing the equilibrium structure for all systems, and computed bulk moduli and relative stabilities were compared to experiments for those compounds where experimental data are available. We found that the results obtained with functionals that take into account dispersive interactions are closer to experiments than those obtained with a bare generalized gradient functional. However, the variation among individual methods is considerable, and functionals that perform well for one quantity may give rather large deviations for another. Taking together the whole body of results, it appears that the Perdew-Burke-Ernzerhof functional including a many-body dispersion correction and the rev-vdW-DF2 methods present the best performance for the description of SiO2 and AlPO4 materials.

Electronic Structure and Band Alignments of Various Phases of Titania Using the Self-Consistent Hybrid Density Functional and DFT+U Methods.

To understand, and thereby rationally optimize photoactive interfaces, it is of great importance to elucidate the electronic structures and band alignments of these interfaces. For the first-principles investigation of these properties, conventional density functional theory (DFT) requires a solution to mitigate its well-known bandgap underestimation problem. Hybrid functional and Hubbard U correction are computationally efficient methods to overcome this limitation, however, the results are largely dependent on the choice of parameters. In this study, we employed recently developed self-consistent approaches, which enable non-empirical determination of the parameters, to investigate TiO2 interfacial systems-the most prototypical photocatalytic systems. We investigated the structural, electronic, and optical properties of rutile and anatase phases of TiO2. We found that the self-consistent hybrid functional method predicts the most reliable structural and electronic properties that are comparable to the experimental and high-level GW results. Using the validated self-consistent hybrid functional method, we further investigated the band edge positions between rutile and anatase surfaces in a vacuum and electrolyte medium, by coupling it with the Poisson-Boltzmann theory. This suggests the possibility of a transition from the straddling-type to the staggered-type band alignment between rutile and anatase phases in the electrolyte medium, manifested by the formation of a Stern-like layer at the interfaces. Our study not only confirms the efficacy of the self-consistent hybrid functional method by reliably predicting the electronic structure of photoactive interfaces, but also elucidates a potentially dramatic change in the band edge positions of TiO2 in aqueous electrolyte medium which can extensively affect its photophysical properties.

Benchmarking several van der Waals dispersion approaches for the description of intermolecular interactions.

Seven methods, including three van der Waals density functionals (vdW-DFs) and four different variants of the Tkatchenko-Scheffler (TS) methods, are tested on the A24, L7, and Taylor et al.'s "blind" test sets. It is found that for these systems, the vdW-DFs perform better that the TS methods. In particular, the vdW-DF-cx functional gives binding energies that are the closest to the reference values, while the many-body correction of TS does not always lead to an improvement in the description of molecular systems. In light of these results, several directions for further improvements to describe van der Waals interactions are discussed.

Failure of Density Functional Dispersion Correction in Metallic Systems and Its Possible Solution Using a Modified Many-Body Dispersion Correction.

Previous density functional dispersion corrections to density functional theory lead to an unphysical description of metallic systems, as exemplified by alkali and alkaline earth compounds. We demonstrate that it is possible to remedy this limitation by including screening effects into the form of interacting smeared-out dipoles in the many-body expansion of the interaction. Our new approach, called the coupled fluctuating smeared dipole model, describes equally well noncovalent systems, such as molecular pairs and crystals, and metallic systems.

First-Principles Study of the α-ß Phase Transition of Ferroelectric Poly(vinylidene difluoride): Observation of Multiple Transition Pathways.

Transition routes from the α (nonpolar) phase to the ß (polar) phase of polyvinylidene difluoride (PVDF) are investigated by first-principles simulation methods. Among various possible routes, including complex torsional and rotational motions, we propose two prototypical transition routes and identify important intermediate structures along each transition pathway using the generalized solid-state nudged elastic band (G-SSNEB) method. The effect of the external electric field and mechanical drawing on the transition behavior is investigated by estimating electric enthalpy and stress tensors. Finite-temperature ab initio molecular dynamics (AIMD) simulations and stress tensor analysis reveal the possibility of enhancement of the crystallinity under hydrodynamic compression.