Chemical Parameter Space Research Articles

The wondrous world of ABX3 molecular perovskites.

The substitution of atoms with molecular building blocks to form hybrid organic-inorganic networks has been an important research theme for several decades. ABX3 molecular perovskites (MolPs) are a subclass of hybrid networks, adopting the perovskite structure with cationic and anionic molecules on the A-site and X-site. MolPs such as ((CH3)2NH2)Zn(HCOO)3 or ((n-C3H7)4N)Mn(C2N3)3 show a range of fascinating structure-chemical properties, including temperature-driven phase transitions that include a change of polarity as interesting for ferroelectrics, pressure-driven order-disorder phase transitions as interesting for barocaloric solid-state refrigeration, and most recently, melting-behaviour before decomposition with subsequent glass formation after cooling. In this feature article, we take a more personal perspective, overviewing the field's current state and outlining future directions. We start by comparing the MolPs' structural chemistry with their inorganic parents, a comparison that helps us identify opportunities for material design. After discussing the MolPs' potential as barocalorics, ferroelectrics, and in the area of glasses, we outline some challenges that lie ahead. Beyond their relevance as a hybrid analogue of inorganic perovskites, we find that MolPs' chemical parameter space provides exciting opportunities for systematically developing design guidelines for functional materials.

High Throughput Multidimensional Kinetic Screening in Continuous Flow Reactors

AbstractAn automated high throughput multidimensional reaction screening platform based on an inline Fourier‐transform infrared spectroscopy is presented. By combining flow chemistry, machine automation and inline analysis, the platform is able to screen reactions in multidimensions (residence time, monomer concentration, degree of polymerization, reaction temperature and monomer conversion) rapidly and efficiently way. Kinetic data libraries associated with high data precision (absolute error <4 %), high reproducibility and high data density are built with ease from the platform. To test the method, we screened the reversible addition‐fragmentation chain transfer polymerization of methyl acrylate in unmatched detail, and the ring opening metathesis polymerization of methyl‐5‐norbornene‐2‐carboxylate. The method we introduce is a key step in providing “big data” for data driven research in the future, and already at present allows for precise prediction of reaction outcomes within the high‐dimensional chemical parameter space that is screened.

High Throughput Multidimensional Kinetic Screening in Continuous Flow Reactors.

An automated high throughput multidimensional reaction screening platform based on an inline Fourier-transform infrared spectroscopy (FT-IR) is presented. By combining flow chemistry, machine automation and inline analysis, the platform is able to screen reactions in multidimensions (residence time, monomer concentration, degree of polymerization, reaction temperature and monomer conversion) rapidly and efficiently way. Kinetic data libraries associated with high data precision (absolute error < 4 %), high reproducibility and high data density are built with ease from the platform. To test the method, we screened the RAFT polymerization of methyl acrylate in unmatched detail, and the ring opening metathesis polymerization of methyl-5-norbornene-2-carboxylate. The method we introduce is a key step in providing 'big data' for data driven research in the future, and already at present allows for precise prediction of reaction outcomes within the high-dimensional chemical parameter space that is screened.

Doped Graphene Quantum Dots UV-vis Absorption Spectrum: A High-Throughput TDDFT Study.

We report on time-dependent density functional theory calculations of the excited states of 63 different graphene quantum dots (GQDs) in square shape with side lengths of 1, 1.5, and 2 nm. We investigate the systematics and trends in the UV-vis absorption spectra of these GQDs, which are doped with elements B, N, O, S, and P at dopant percentages of 1.5%, 3%, 5%, and 7%. The results show how the peaks in the UV and visible parts of the spectrum as well as the total absorption evolve in the chemical parameter space along the coordinates of size, dopant type, and dopant percentage. The absorption spectra calculated here can be used to obtain particular GQD mixture proportions that would yield a desired absorption profile such as flat absorption across the whole visible spectrum or one that is locally peaked around a chosen wavelength.

Phase Control in the Modulated Self‐Assembly of Lanthanide MOFs of a Flexible Tetratopic Bis‐Amide Linker

AbstractThe targeted synthesis of phase‐pure metal‐organic frameworks (MOFs) is often complicated by the fact that a number of different products can result from the same metal‐ligand combination. The self‐assembly of lanthanide‐based MOFs can be particularly challenging due to the versatile coordination chemistry of the f‐block allowing for diverse coordination spheres with irregular geometries driven by steric effects. Herein we show that modulated self‐assembly – the addition of small molecules to solvothermal synthesis in order to control pH and coordination equilibria – can control phase formation when preparing lanthanide MOFs from the tetratopic bis‐(3,5‐dicarboxyphenyl)terephthalamide ligand. We report 27 new MOFs, of which 21 are characterised by single crystal X‐ray diffraction, that can be categorised into six structure types. The formation of different phases is largely driven by the contraction in ionic radius across the lanthanide series, but we show how careful choice of both solvent and modulator can influence reaction outcome. As an example, phase pure samples of three Gd MOFs of the same ligand can be selected by simply changing the modulator – acetic acid, benzoic acid, or nitric acid – used in closely related syntheses. These results show that modulation can control phase in f‐block MOFs, just as it can in d‐block MOFs, and provide design rules for uncovering structural diversity by carefully exploring the chemical parameter space of lanthanide MOFs in single ligand systems.

Machine-Learning-Accelerated Perovskite Crystallization

Summary Perovskites have seen significant research interest in the last decade. As ternary and quaternary compounds, their chemical space is exceptionally large, yet perovskite development has been limited to a restricted set of chemical constituents often discovered through trial and error. Here, we report a high-throughput experimental framework for the discovery of new perovskite single crystals. We use machine learning (ML) to guide the sequence of ever-improved robotic synthetic trials. We perform high-throughput syntheses of perovskite single crystals with a protein crystallization robot and characterize the outcomes with the aid of convolutional neural network-based image recognition. We then use an ML model to predict the optimal conditions for the synthesis of a new perovskite single crystal, enabling us to report the first synthesis of (3-PLA)2PbCl4.This material exhibits strong blue emission, illustrating the applicability of the method in identifying new optoelectronic materials.

Diabatic models with transferrable parameters for generalized chemical reactions

Diabatic models applied to adiabatic electron-transfer theory yield many equations involving just a few parameters that connect ground-state geometries and vibration frequencies to excited-state transition energies and vibration frequencies to the rate constants for electron-transfer reactions, utilizing properties of the conical-intersection seam linking the ground and excited states through the Pseudo Jahn-Teller effect. We review how such simplicity in basic understanding can also be obtained for general chemical reactions. The key feature that must be recognized is that electron-transfer (or hole transfer) processes typically involve one electron (hole) moving between two orbitals, whereas general reactions typically involve two electrons or even four electrons for processes in aromatic molecules. Each additional moving electron leads to new high-energy but interrelated conical-intersection seams that distort the shape of the critical lowest-energy seam. Recognizing this feature shows how conical-intersection descriptors can be transferred between systems, and how general chemical reactions can be compared using the same set of simple parameters. Mathematical relationships are presented depicting how different conical-intersection seams relate to each other, showing that complex problems can be reduced into an effective interaction between the ground-state and a critical excited state to provide the first semi-quantitative implementation of Shaik’s “twin state” concept. Applications are made (i) demonstrating why the chemistry of the first-row elements is qualitatively so different to that of the second and later rows, (ii) deducing the bond-length alternation in hypothetical cyclohexatriene from the observed UV spectroscopy of benzene, (iii) demonstrating that commonly used procedures for modelling surface hopping based on inclusion of only the first-derivative correction to the Born-Oppenheimer approximation are valid in no region of the chemical parameter space, and (iv), demonstrating the types of chemical reactions that may be suitable for exploitation as a chemical qubit in some quantum information processor.

Mirror symmetry breaking with limited enantioselective autocatalysis and temperature gradients: a stability survey

We analyze limited enantioselective (LES) autocatalysis in a temperature gradient and with internal flow/recycling of hot and cold material. Microreversibility forbids broken mirror symmetry for LES in the presence of a temperature gradient alone. This symmetry can be broken however when the auto-catalysis and limited enantioselective catalysis are each localized within the regions of low and high temperature, respectively. This scheme has been recently proposed as a plausible model for spontaneous emergence of chirality in abyssal hydrothermal vents. Regions in chemical parameter space are mapped out in which the racemic state is unstable and bifurcates to chiral solutions.

Microfluidic platform for combinatorial synthesis in picolitre droplets

This paper presents a droplet-based microfluidic platform for miniaturized combinatorial synthesis. As a proof of concept, a library of small molecules for early stage drug screening was produced. We present an efficient strategy for producing a 7 × 3 library of potential thrombin inhibitors that can be utilized for other combinatorial synthesis applications. Picolitre droplets containing the first type of reagent (reagents A(1), A(2), …, A(m)) were formed individually in identical microfluidic chips and then stored off chip with the aid of stabilizing surfactants. These droplets were then mixed to form a library of droplets containing reagents A(1-m), each individually compartmentalized, which was reinjected into a second microfluidic chip and combinatorially fused with picolitre droplets containing the second reagent (reagents B(1), B(2), …, B(n)) that were formed on chip. The concept was demonstrated with a three-component Ugi-type reaction involving an amine (reagents A(1-3)), an aldehyde (reagents B(1-7)), and an isocyanide (held constant), to synthesize a library of small molecules with potential thrombin inhibitory activity. Our technique produced 10(6) droplets of each reaction at a rate of 2.3 kHz. Each droplet had a reaction volume of 3.1 pL, at least six orders of magnitude lower than conventional techniques. The droplets can then be divided into aliquots for different downstream screening applications. In addition to medicinal chemistry applications, this combinatorial droplet-based approach holds great potential for other applications that involve sampling large areas of chemical parameter space with minimal reagent consumption; such an approach could be beneficial when optimizing reaction conditions or performing combinatorial reactions aimed at producing novel materials.

Special issue in flow chemistry

In the last decade, we havewitnessed a steady transformation of organic chemistry. The number of launched new chemical entities decreased in spite of the number of synthetically accessible small molecules that exponentially increased. On the other hand, the drug like chemical space was only poorly sampled and the huge compound decks at Big Pharma companies were saturated with redundant structures. As a consequence, new synthetic techniqueswere required and emerged which were collectively called “enabling chemistry” [1]. Enabling chemistry methods contribute to accelerate chemical processes, explore new chemistries, increase the throughput and facilitate the workup and isolation. Such processes also meet several criteria of Green chemistry (i.e., increased reaction rate, reducing waste, and increased atom economy) and contribute to the development of sustainablemanufacturing (i.e., recycling, biomass transformation, etc.). Enabling chemistry led the way to continuous flow chemistry and microreactors [2]. While the significant advancement in immobilized catalysts and supported reagents naturally found application in continuous flow systems (i.e., fixedbed microreactors) the wide-spread application of microwave-assisted organic synthesis (MAOS) promoted the further expansion of the boundaries of the chemical parameter space [3]. However, the batch nature of synthetic organic chemistry and the chemical parameter space it covers has not changed during the ca. 200 years’ history of organic synthesis and only a small fraction of the parameter space is covered