Machine-Learning-Assisted Synthesis of Polar Racemates

Copper-Based Two-Dimensional Metal-Organic Frameworks for Fenton-like Photocatalytic Degradation of Methylene Blue under UV and Sunlight Irradiation

To efficiently degrade organic pollutants, photocatalysts must be effective under both ultraviolet (UV) radiation and sunlight. We synthesized a series of new metal-organic frameworks by using mild hydrothermal conditions. These frameworks incorporate three distinct bipyridyl ligands: pyrazine (pyr), 4,4'-bipyridine (bpy), and 1,2-bis(4-pyridyl)ethane (bpe). The resulting compounds are denoted as [Cu(pyz)(H2O)2MF6], [Cu(bpy)2(H2O)2]·MF6, and [Cu(bpe)2(H2O)2]·MF6·H2O [M = Zr (1, 3, and 5) and Hf (2, 4, and 6)]. All six compounds exhibited a two-dimensional crystal structure comprising infinitely nonintersecting linear chains. Compound 3 achieved 100% degradation of methylene blue (MB) after 8 min under UV irradiation and 100 min under natural sunlight in the presence of H2O2 as the electron acceptor. For compound 5, 100% MB degradation was achieved after 120 min under sunlight and 10 min under UV light. Moreover, reactive radical tests revealed that the dominant species involved in photocatalytic degradation are hydroxyl (•OH), superoxide radicals (•O2-), and photogenerated holes (h+). The photodegradation process followed pseudo-first-order kinetics, with photodegradation rate constants of 0.362 min-1 (0.039 min-1) for 3 and 0.316 min-1 (0.033 min-1) for 5 under UV (sunlight) irradiation. The developed photocatalysts with excellent activity and good recyclability are promising green catalysts for degrading organic pollutants during environmental decontamination.

Beyond π-π Stacking: Understanding Inversion Symmetry Breaking in Crystalline Racemates

The design of noncentrosymmetric (NCS) solid state materials, specifically how to break inversion symmetry between enantiomers, has intrigued chemists, physicists, and materials scientists for many years. Because the chemical complexity of molecular racemic building units is so varied, targeting these materials is poorly understood. Previously, three isostructural racemic compounds with a formula of [Cu(H₂O)(bpy)₂]₂[MF₆]₂·2H₂O (bpy = 2,2'=bipyridine; M = Ti, Zr, Hf) were shown to crystallize in the NCS space group Pna2₁, of polar, achiral crystal class mm2. In this work, we synthesized five new racemic compounds with the formula [Cu(H₂O)(dmbpy)₂]₂[MF₆]₂·xH₂O (dmbpy = 4,4'/5,5'-dimethyl-2,2'-bipyridine; M = Ti, Zr, Hf). Single crystal X-ray diffraction reveals that the five newly synthesized compounds feature equimolar combinations of Δ- and Λ-Cu(dmbpy)₂(H₂O)²⁺ complexes that are assembled into packing motifs similar to those found in the reported NCS structure but all crystallize in centrosymmetric (CS) space groups. Seven structural descriptors were created to analyze the intermolecular interactions on the assembly of Cu racemates in the CS and NCS structures. The structural analysis reveals that in the CS structures, the inversion center results from parallel heterochiral π-π stacking interactions between adjacent Cu racemates regardless of cation geometries, hydrogen bonding networks, or interlayer architectures, whereas in the NCS structure, nonparallel heterochiral π-π interactions between the adjacent Cu racemates preclude an inversion center. The parallel heterochiral π-π interactions in the CS structures can be rationalized by the restrained geometries of the methyl-substituted ligands. This work demonstrates that the introduction of nonparallel stacking can suppress the formation of an inversion center for an NCS racemate. A conceptual framework and practical approach linking the absence of inversion symmetry in racemates is presented for all NCS crystal classes.

Data storage architectures to accelerate chemical discovery: data accessibility for individual laboratories and the community

As buzzwords like "big data," "machine learning," and "high-throughput" expand through chemistry, chemists need to consider more than ever their data storage, data management, and data accessibility, whether in their own laboratories or with the broader community. While it is commonplace for chemists to use spreadsheets for data storage and analysis, a move towards database architectures ensures that the data can be more readily findable, accessible, interoperable, and reusable (FAIR). However, making this move has several challenges for those with limited-to-no knowledge of computer programming and databases. This Perspective presents basics of data management using databases with a focus on chemical data. We overview database fundamentals by exploring benefits of database use, introducing terminology, and establishing database design principles. We then detail the extract, transform, and load process for database construction, which includes an overview of data parsing and database architectures, spanning Standard Query Language (SQL) and No-SQL structures. We close by cataloging overarching challenges in database design. This Perspective is accompanied by an interactive demonstration available at https://github.com/D3TaLES/databases_demo. We do all of this within the context of chemical data with the aim of equipping chemists with the knowledge and skills to store, manage, and share their data while abiding by FAIR principles.

Design of Organic-Inorganic Hybrid Heterostructured Semiconductors via High-Throughput Materials Screening for Optoelectronic Applications

Organic-inorganic hybrid semiconductors, of which organometal halide perovskites are representative examples, have drawn significant research interest as promising candidates for next-generation optoelectronic applications. This interest is mainly ascribed to the emergent optoelectronic properties of the hybrid semiconductors that are distinct from those of their purely inorganic and organic counterparts as well as different material fabrication strategies and the other material (e.g., mechanical) properties that combine the advantages of both. Herein, we present a high-throughput first-principles material screening study of the hybrid heterostructured semiconductors (HHSs) that differ entirely from organometal halide perovskite hybrid ion-substituting semiconductors. HHSs crystallize as superlattice structures composed of inorganic tetrahedrally coordinated semiconductor sublayers and organic sublayers made of bidentate chain-like molecules. By changing the composition (e.g., IV, III-V, II-VI, I-III-VI₂ semiconductor) and polymorph (e.g., wurtzite and zinc-blende) of the inorganic components, the type of organic molecules (e.g., ethylenediamine, ethylene glycol, and ethanedithiol), and the thickness of the composing layers across 234 candidate HHSs, we investigated their thermodynamic, electronic structure, and optoelectronic properties. Thermodynamic stability analysis indicates the existence of 96 stable HHSs beyond the ZnTe/ZnSe-based ones synthesized experimentally. The electronic structure and optoelectronic properties of HHSs can be modulated over a wide range by manipulating their structural variants. A machine learning approach was further applied to the high-throughput calculated data to identify the critical descriptors determining thermodynamic stability and electronic band gap. Our results indicate promising prospects and provide valuable guidance for the rational design of organic-inorganic hybrid heterostructured semiconductors for potential optoelectronic applications.

Predicting the Redox Potentials of Phenazine Derivatives Using DFT-Assisted Machine Learning

This study investigates four machine-learning (ML) models to predict the redox potentials of phenazine derivatives in dimethoxyethane using density functional theory (DFT). A small data set of 151 phenazine derivatives having only one type of functional group per molecule (20 unique groups) was used for the training. Prediction accuracy was improved by a combined strategy of feature selection and hyperparameter optimization, using the external validation set. Models were evaluated on the external test set containing new functional groups and diverse molecular structures. High prediction accuracies of R ² > 0.74 were obtained on the external test set. Despite being trained on the molecules with a single type of functional group, models were able to predict the redox potentials of derivatives containing multiple and different types of functional groups with good accuracies (R ² > 0.7). This type of performance for predicting redox potential from such a small and simple data set of phenazine derivatives has never been reported before. Redox flow batteries (RFBs) are emerging as promising candidates for energy storage systems. However, new green and efficient materials are required for their widespread usage. We believe that the hybrid DFT-ML approach demonstrated in this report would help in accelerating the virtual screening of phenazine derivatives, thus saving computational and experimental costs. Using this approach, we have identified promising phenazine derivatives for green energy storage systems such as RFBs.

Design of Organic Electronic Materials With a Goal-Directed Generative Model Powered by Deep Neural Networks and High-Throughput Molecular Simulations

In recent years, generative machine learning approaches have attracted significant attention as an enabling approach for designing novel molecular materials with minimal design bias and thereby realizing more directed design for a specific materials property space. Further, data-driven approaches have emerged as a new tool to accelerate the development of novel organic electronic materials for organic light-emitting diode (OLED) applications. We demonstrate and validate a goal-directed generative machine learning framework based on a recurrent neural network (RNN) deep reinforcement learning approach for the design of hole transporting OLED materials. These large-scale molecular simulations also demonstrate a rapid, cost-effective method to identify new materials in OLEDs while also enabling expansion into many other verticals such as catalyst design, aerospace, life science, and petrochemicals.

Machine Learning Guided Design of Single-Phase Hybrid Lead Halide White Phosphors

Designing new single-phase white phosphors for solid-state lighting is a challenging trial-error process as it requires to navigate in a multidimensional space (composition of the host matrix/dopants, experimental conditions, etc.). Thus, no single-phase white phosphor has ever been reported to exhibit both a high color rendering index (CRI - degree to which objects appear natural under the white illumination) and a tunable correlated color temperature (CCT). In this article, a novel strategy consisting in iterating syntheses, characterizations, and machine learning (ML) models to design such white phosphors is demonstrated. With the guidance of ML models, a series of luminescent hybrid lead halides with ultra-high color rendering (above 92) mimicking the light of the sunrise/sunset (CCT = 3200 K), morning/afternoon (CCT = 4200 K), midday (CCT = 5500 K), full sun (CCT = 6500K), as well as an overcast sky (CCT = 7000 K) are precisely designed.

Crystal structures of two copper(I)-6,6'-dimethyl-2,2'-bipyridyl (dmbpy) compounds, [Cu(dmbpy)2]2[MF6]·xH2O (M = Zr, Hf; x = 1.134, 0.671)

The syntheses and crystal structures of two bimetallic mol-ecular compounds, namely, bis[bis-(6,6'-dimethyl-2,2'-bi-pyridine)-copper(I)] hexa-fluorido-zir-con-ate(IV) 1.134-hydrate, [Cu(dmbpy)₂]₂[ZrF₆]·1.134H₂O (dmbpy = 6,6'-di-methyl-2,2'-bipyri-dyl, C₁₂H₁₂N₂), (I), and bis[bis-(6,6'-dimethyl-2,2'-bi-pyr-idine)-copper(I)] hexa-fluorido-hafnate(IV) 0.671-hydrate, [Cu(dmbpy)₂]₂[HfF₆]·0.671H₂O, (II), are reported. Apart from a slight site occupany difference for the water mol-ecule of crystallization, compounds (I) and (II) are isostructural, featuring isolated tetra-hedral cations of copper(I) ions coordinated by two dmbpy ligands and centrosymmetric, octa-hedral anions of fluorinated early transition metals. The tetra-hedral environments of the copper complexes are distorted owing to the steric effects of the dmbpy ligands. The extended structures are built up through Coulombic inter-actions between cations and anions and π-π stacking inter-actions between heterochiral Δ- and Λ-[Cu(dmbpy)₂]⁺ complexes. A comparison between the title compounds and other [Cu(dmbpy)₂]⁺ compounds with monovalent and bivalent anions reveals a significant influence of the cation-to-anion ratio on the resulting crystal packing architectures, providing insights for future crystal design of distorted tetra-hedral copper compounds.

Crystal structures of three copper(II)-2,2'-bi-pyridine (bpy) compounds, [Cu(bpy)2(H2O)][SiF6]·4H2O, [Cu(bpy)2(TaF6)2] and [Cu(bpy)3][TaF6]2 and a related coordination polymer, [Cu(bpy)(H2O)2SnF6] n

We report the hydro-thermal syntheses and crystal structures of aqua-bis-(2,2'-bi-pyridine-κ² N,N')copper(II) hexa-fluorido-silicate tetra-hydrate, [Cu(bpy)₂(H₂O)][SiF₆]·4H₂O (bpy is 2,2'-bi-pyridine, C₁₀H₈N₂), (I), bis-(2,2'-bi-pyridine-3κ² N,N')-di-μ-fluorido-1:3κ² F:F;2:3κ² F:F-deca-fluorido-1κ⁵ F,2κ⁵ F-ditantalum(V)copper(II), [Cu(bpy)₂(TaF₆)₂], (II), tris-(2,2'-bi-pyridine-κ² N,N')copper(II) bis[hexa-fluorido-tantalate(V)], [Cu(bpy)₃][TaF₆]₂, (III), and catena-poly[[di-aqua-(2,2'-bi-pyridine-κ² N,N')copper(II)]-μ-fluorido-tetra-fluorido-tin-μ-fluorido], [Cu(bpy)(H₂O)₂SnF₆] _n , (IV). Compounds (I), (II) and (III) contain locally chiral copper coordination complexes with C ₂, D ₂, and D ₃ symmetry, respectively. The extended structures of (I) and (IV) are consolidated by O-H⋯F and O-H⋯O hydrogen bonds. The structure of (III) was found to be a merohedral (racemic) twin.

Multimodal Structure Solution with 19F NMR Crystallography of Spin Singlet Molybdenum Oxyfluorides

Complex crystal structures with subtle atomic-scale details are now routinely solved using complementary tools such as X-ray and/or neutron scattering combined with electron diffraction and imaging. Identifying unambiguous atomic models for oxyfluorides, needed for materials design and structure-property control, is often still a considerable challenge despite their advantageous optical responses and applications in energy storage systems. In this work, NMR crystallography and single-crystal X-ray diffraction are combined for the complete structure solution of three new compounds featuring a rare triangular early transition metal oxyfluoride cluster, [Mo₃O₄F₉]^5-. After framework identification by single-crystal X-ray diffraction, 1D and 2D solid-state ¹⁹F NMR spectroscopy supported by ab initio calculations are used to solve the structures of K₅[Mo₃O₄F₉]·3H₂O (1), K₅[Mo₃O₄F₉]·2H₂O (2), and K₁₆[Mo₃O₄F₉]₂[TiF₆]₃·2H₂O (3) and to assign the nine distinct fluorine sites in the oxyfluoride clusters. Furthermore, ¹⁹F NMR identifies selective fluorine dynamics in K₁₆[Mo₃O₄F₉]₂[TiF₆]₃·2H₂O. These dual scattering and spectroscopy methods are used to demonstrate the generality and sensitivity of ¹⁹F shielding to small changes in bond length, on the order of 0.01 Å or less, even in the presence of hydrogen bonding, metal-metal bonding, and electrostatic interactions. Starting from the structure models, the nature of chemical bonding in the molybdates is explained by molecular orbital theory and electronic structure calculations. The average Mo-Mo distance of 2.505 Å and diamagnetism in 1, 2, and 3 are attributed to a metal-metal bond order of unity along with a 1a²1e⁴ electronic ground state configuration for the [Mo₃O₄F₉]^5- cluster, leading to a rare trimeric spin singlet involving d² Mo⁴⁺ ions. The approach to structure solution and bonding analysis is a powerful strategy for understanding the structures and chemical properties of complex fluorides and oxyfluorides.

Topotactic desolvation and condensation reactions of 3D Zn3TiF7(H2O)2(taz)3·S (S = 3H2O or C2H5OH)

Thermodiffraction, IR, DFT calculations, and ¹H and ¹⁹F NMR characterizations of the desolvatation and reversible condensation reactions of Zn₃TiF₇(taz)₃ family.