The seventh blind test of crystal structure prediction: structure ranking methods

A seventh blind test of crystal structure prediction has been organized by the Cambridge Crystallographic Data Centre. The results are presented in two parts, with this second part focusing on methods for ranking crystal structures in order of stability. The exercise involved standardized sets of structures seeded from a range of structure generation methods. Participants from 22 groups applied several periodic DFT-D methods, machine learned potentials, force fields derived from empirical data or quantum chemical calculations, and various combinations of the above. In addition, one non-energy-based scoring function was used. Results showed that periodic DFT-D methods overall agreed with experimental data within expected error margins, while one machine learned model, applying system-specific AIMnet potentials, agreed with experiment in many cases demonstrating promise as an efficient alternative to DFT-based methods. For target XXXII, a consensus was reached across periodic DFT methods, with consistently high predicted energies of experimental forms relative to the global minimum (above 4 kJ mol-1 at both low and ambient temperatures) suggesting a more stable polymorph is likely not yet observed. The calculation of free energies at ambient temperatures offered improvement of predictions only in some cases (for targets XXVII and XXXI). Several avenues for future research have been suggested, highlighting the need for greater efficiency considering the vast amounts of resources utilized in many cases.

The seventh blind test of crystal structure prediction: structure generation methods

A seventh blind test of crystal structure prediction was organized by the Cambridge Crystallographic Data Centre featuring seven target systems of varying complexity: a silicon and iodine-containing molecule, a copper coordination complex, a near-rigid molecule, a cocrystal, a polymorphic small agrochemical, a highly flexible polymorphic drug candidate, and a polymorphic morpholine salt. In this first of two parts focusing on structure generation methods, many crystal structure prediction (CSP) methods performed well for the small but flexible agrochemical compound, successfully reproducing the experimentally observed crystal structures, while few groups were successful for the systems of higher complexity. A powder X-ray diffraction (PXRD) assisted exercise demonstrated the use of CSP in successfully determining a crystal structure from a low-quality PXRD pattern. The use of CSP in the prediction of likely cocrystal stoichiometry was also explored, demonstrating multiple possible approaches. Crystallographic disorder emerged as an important theme throughout the test as both a challenge for analysis and a major achievement where two groups blindly predicted the existence of disorder for the first time. Additionally, large-scale comparisons of the sets of predicted crystal structures also showed that some methods yield sets that largely contain the same crystal structures.

Sublimation of pyridine derivatives: fundamental aspects and application for two-component crystal screening

The saturated vapour pressures of five heterocyclic compounds containing the pyridine fragment, namely, three isomers of aminopyridine (2-aminopyridine (2AmPy), 3-aminopyridine (3AmPy), and 4-aminopyridine (4AmPy)); 3-hydroxypyridine (3OHPy) and 2-(1H-imidazol-2-yl)pyridine (ImPy), were measured at appropriate temperature intervals using a transpiration (inert gas flow) method. The standard molar enthalpies, entropies, and Gibbs energies of sublimation for all the studied substances were determined. Among the compounds studied, the largest value of ΔH298sub was observed for ImPy. The influence of substitution and the effects of hydrogen bonds in the crystal lattices on sublimation parameters are discussed herein. The reliable dependences relating ΔG298sub to Tfus and ΔH298sub to ΔG298sub were plotted. A comparative analysis of several calculation schemes for the estimation of sublimation enthalpy and Gibbs free energy was carried out. Thermodynamic parameters obtained in this study were applied for the evaluation of cocrystallisation thermodynamic functions for two-component crystals (virtual screening) on the basis of the studied substituted pyridines.

Unprecedented Packing Polymorphism of Oxindole: An Exploration Inspired by Crystal Structure Prediction

Crystal polymorphism, characterized by different packing arrangements of the same compound, strongly ties to the physical properties of a molecule. Determining the polymorphic landscape is complex and time-consuming, with the number of experimentally observed polymorphs varying widely from molecule to molecule. Furthermore, disappearing polymorphs, the phenomenon whereby experimentally observed forms cannot be reproduced, pose a significant challenge for the pharmaceutical industry. Herein, we focused on oxindole (OX), a small rigid molecule with four known polymorphs, including a reported disappearing form. Using crystal structure prediction (CSP), we assessed OX solid-state landscape and thermodynamic stability by comparing predicted structures with experimentally known forms. We then performed melt and solution crystallization in bulk and nanoconfinement to validate our predictions. These experiments successfully reproduced the known forms and led to the discovery of four novel polymorphs. Our approach provided insights into reconstructing disappearing polymorphs and building more comprehensive polymorph landscapes. These results also establish a new record of packing polymorphism for rigid molecules.

Towards routine organic structure determination using Raman microscopy

Raman microscopy can reveal a compound-specific vibrational "fingerprint" from micrograms of material with no sample preparation. We expect this increasingly available instrumentation to routinely assist synthetic chemists in structure determination; however, interpreting the information-dense spectra can be challenging for unreported compounds. Appropriate theoretical calculations using the highly efficient r2SCAN-3c method can accurately predict peak positions but are less precise in matching peak heights. To limit incorrect biases while comparing experimental and theoretical spectra, we introduce a user-friendly software that gives a match score to assist with structure determination. The capabilities and limitations of this approach are demonstrated for several proof-of-concept examples including the characterization of intermediates in the total synthesis of deoxyaspidodispermine.

Frontiers of molecular crystal structure prediction for pharmaceuticals and functional organic materials

The reliability of organic molecular crystal structure prediction has improved tremendously in recent years. Crystal structure predictions for small, mostly rigid molecules are quickly becoming routine. Structure predictions for larger, highly flexible molecules are more challenging, but their crystal structures can also now be predicted with increasing rates of success. These advances are ushering in a new era where crystal structure prediction drives the experimental discovery of new solid forms. After briefly discussing the computational methods that enable successful crystal structure prediction, this perspective presents case studies from the literature that demonstrate how state-of-the-art crystal structure prediction can transform how scientists approach problems involving the organic solid state. Applications to pharmaceuticals, porous organic materials, photomechanical crystals, organic semi-conductors, and nuclear magnetic resonance crystallography are included. Finally, efforts to improve our understanding of which predicted crystal structures can actually be produced experimentally and other outstanding challenges are discussed.

Review of Applications of Density Functional Theory (DFT) Quantum Mechanical Calculations to Study the High-Pressure Polymorphs of Organic Crystalline Materials

Since its inception, chemistry has been predominated by the use of temperature to generate or change materials, but applications of pressure of more than a few tens of atmospheres for such purposes have been rarely observed. However, pressure is a very effective thermodynamic variable that is increasingly used to generate new materials or alter the properties of existing ones. As computational approaches designed to simulate the solid state are normally tuned using structural data at ambient pressure, applying them to high-pressure issues is a highly challenging test of their validity from a computational standpoint. However, the use of quantum chemical calculations, typically at the level of density functional theory (DFT), has repeatedly been shown to be a great tool that can be used to both predict properties that can be later confirmed by experimenters and to explain, at the molecular level, the observations of high-pressure experiments. This article's main goal is to compile, analyze, and synthesize the findings of works addressing the use of DFT in the context of molecular crystals subjected to high-pressure conditions in order to give a general overview of the possibilities offered by these state-of-the-art calculations.

Reducing overprediction of molecular crystal structures via threshold clustering

Crystal structure prediction is becoming an increasingly valuable tool for assessing polymorphism of crystalline molecular compounds, yet invariably, it overpredicts the number of polymorphs. One of the causes for this overprediction is in neglecting the coalescence of potential energy minima, separated by relatively small energy barriers, into a single basin at finite temperature. Considering this, we demonstrate a method underpinned by the threshold algorithm for clustering potential energy minima into basins, thereby identifying kinetically stable polymorphs and reducing overprediction.

Vapor Sorption and Halogen-Bond-Induced Solid-Form Rearrangement of a Porous Pharmaceutical

A porous, nonsolvated polymorph of the voltage-gated sodium channel blocker mexiletine hydrochloride absorbs iodine vapor to give a pharmaceutical cocrystal incorporating an I₂Cl^- anion that forms a halogen-π interaction with the mexiletine cations. The most thermodynamically stable form of the compound does not absorb iodine. This example shows that vapor sorption is a potentially useful and underused tool for bringing about changes in pharmaceutical solid form as part of a solid form screening protocol.

XDM-corrected hybrid DFT with numerical atomic orbitals predicts molecular crystal lattice energies with unprecedented accuracy

Molecular crystals are important for many applications, including energetic materials, organic semiconductors, and the development and commercialization of pharmaceuticals. The exchange-hole dipole moment (XDM) dispersion model has shown good performance in the calculation of relative and absolute lattice energies of molecular crystals, although it has traditionally been applied in combination with plane-wave/pseudopotential approaches. This has limited XDM to use with semilocal functional approximations, which suffer from delocalization error and poor quality conformational energies, and to systems with a few hundreds of atoms at most due to unfavorable scaling. In this work, we combine XDM with numerical atomic orbitals, which enable the efficient use of XDM-corrected hybrid functionals for molecular crystals. We test the new XDM-corrected functionals for their ability to predict the lattice energies of molecular crystals for the X23 set and 13 ice phases, the latter being a particularly stringent test. A composite approach using a XDM-corrected, 25% hybrid functional based on B86bPBE achieves a mean absolute error of 0.48 kcal mol^-1 per molecule for the X23 set and 0.19 kcal mol^-1 for the total lattice energies of the ice phases, compared to recent diffusion Monte-Carlo data. These results make the new XDM-corrected hybrids not only far more computationally efficient than previous XDM implementations, but also the most accurate density-functional methods for molecular crystal lattice energies to date.

From Powders to Single Crystals: A Crystallographer's Toolbox for Small-Molecule Structure Determination

Although the crystal structures of small-molecule compounds are often determined from single-crystal X-ray diffraction (scXRD), recent advances in three-dimensional electron diffraction (3DED) and crystal structure prediction (CSP) methods promise to expand the structure elucidation toolbox available to the crystallographer. Herein, a comparative assessment of scXRD, 3DED, and CSP in combination with powder X-ray diffraction is carried out on two former drug candidate compounds and a multicomponent crystal of a key building block in the synthesis of gefapixant citrate.

Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization

Artificial intelligence and specifically machine learning applications are nowadays used in a variety of scientific applications and cutting-edge technologies, where they have a transformative impact. Such an assembly of statistical and linear algebra methods making use of large data sets is becoming more and more integrated into chemistry and crystallization research workflows. This review aims to present, for the first time, a holistic overview of machine learning and cheminformatics applications as a novel, powerful means to accelerate the discovery of new crystal structures, predict key properties of organic crystalline materials, simulate, understand, and control the dynamics of complex crystallization process systems, as well as contribute to high throughput automation of chemical process development involving crystalline materials. We critically review the advances in these new, rapidly emerging research areas, raising awareness in issues such as the bridging of machine learning models with first-principles mechanistic models, data set size, structure, and quality, as well as the selection of appropriate descriptors. At the same time, we propose future research at the interface of applied mathematics, chemistry, and crystallography. Overall, this review aims to increase the adoption of such methods and tools by chemists and scientists across industry and academia.

A complete description of thermodynamic stabilities of molecular crystals

Predicting stable polymorphs of molecular crystals remains one of the grand challenges of computational science. Current methods invoke approximations to electronic structure and statistical mechanics and thus fail to consistently reproduce the delicate balance of physical effects determining thermodynamic stability. We compute the rigorous ab initio Gibbs free energies for competing polymorphs of paradigmatic compounds, using machine learning to mitigate costs. The accurate description of electronic structure and full treatment of quantum statistical mechanics allow us to predict the experimentally observed phase behavior. This constitutes a key step toward the first-principles design of functional materials for applications from photovoltaics to pharmaceuticals.

Predictions of relative stabilities of (competing) molecular crystals are of great technological relevance, most notably for the pharmaceutical industry. However, they present a long-standing challenge for modeling, as often minuscule free energy differences are sensitively affected by the description of electronic structure, the statistical mechanics of the nuclei and the cell, and thermal expansion. The importance of these effects has been individually established, but rigorous free energy calculations for general molecular compounds, which simultaneously account for all effects, have hitherto not been computationally viable. Here we present an efficient “end to end” framework that seamlessly combines state-of-the art electronic structure calculations, machine-learning potentials, and advanced free energy methods to calculate ab initio Gibbs free energies for general organic molecular materials. The facile generation of machine-learning potentials for a diverse set of polymorphic compounds—benzene, glycine, and succinic acid—and predictions of thermodynamic stabilities in qualitative and quantitative agreement with experiments highlight that predictive thermodynamic studies of industrially relevant molecular materials are no longer a daunting task.

Progress in understanding crystallisation: a personal perspective

After this discussion meeting, most participants felt that we do not understand crystallisation. However, in the 1980s I believe that most scientists would have considered that crystallisation was adequately understood....

The extensive solid-form landscape of sulfathiazole: geometrical similarity and interaction energies

Sulfathiazole shows one of the most extensive solid-form landscapes known to date for an active pharmaceutical ingredient. A standardised structure set of 5 polymorphs, 59 co-crystals, 29 salts, and 3 other structures is established.

Along the road to Crystal Structure Prediction (CSP) of pharmaceutical-like molecules

Computational methods used for predicting crystal structures of organic compounds are mature enough to be routinely used with many rigid and semi-rigid organic molecules. The usefulness of Crystal Structure Prediction...

Quantum Mechanical-Based Stability Evaluation of Crystal Structures for HIV-Targeted Drug Cabotegravir

The long-acting parenteral formulation of the HIV integrase inhibitor cabotegravir (GSK744) is currently being developed to prevent HIV infections, benefiting from infrequent dosing and high efficacy. The crystal structure can affect the bioavailability and efficacy of cabotegravir. However, the stability determination of crystal structures of GSK744 have remained a challenge. Here, we introduced an ab initio protocol to determine the stability of the crystal structures of pharmaceutical molecules, which were obtained from crystal structure prediction process starting from the molecular diagram. Using GSK744 as a case study, the ab initio predicted that Gibbs free energy provides reliable further refinement of the predicted crystal structures and presents its capability for becoming a crystal stability determination approach in the future. The proposed work can assist in the comprehensive screening of pharmaceutical design and can provide structural predictions and stability evaluation for pharmaceutical crystals.

High-throughput virtual screening for organic electronics: a comparative study of alternative strategies

We present a review of the field of high-throughput virtual screening for organic electronics materials focusing on the sequence of methodological choices that determine each virtual screening protocol. These choices are present in all high-throughput virtual screenings and addressing them systematically will lead to optimised workflows and improve their applicability. We consider the range of properties that can be computed and illustrate how their accuracy can be determined depending on the quality and size of the experimental datasets. The approaches to generate candidates for virtual screening are also extremely varied and their relative strengths and weaknesses are discussed. The analysis of high-throughput virtual screening is almost never limited to the identification of top candidates and often new patterns and structure-property relations are the most interesting findings of such searches. The review reveals a very dynamic field constantly adapting to match an evolving landscape of applications, methodologies and datasets.

Computational Tools to Rationalize and Predict the Self-Assembly Behavior of Supramolecular Gels

Supramolecular gels form a class of soft materials that has been heavily explored by the chemical community in the past 20 years. While a multitude of experimental techniques has demonstrated its usefulness when characterizing these materials, the potential value of computational techniques has received much less attention. This review aims to provide a complete overview of studies that employ computational tools to obtain a better fundamental understanding of the self-assembly behavior of supramolecular gels or to accelerate their development by means of prediction. As such, we hope to stimulate researchers to consider using computational tools when investigating these intriguing materials. In the concluding remarks, we address future challenges faced by the field and formulate our vision on how computational methods could help overcoming them.

Toward Physics-Based Solubility Computation for Pharmaceuticals to Rival Informatics

We demonstrate that physics-based calculations of intrinsic aqueous solubility can rival cheminformatics-based machine learning predictions. A proof-of-concept was developed for a physics-based approach via a sublimation thermodynamic cycle, building upon previous work that relied upon several thermodynamic approximations, notably the 2RT approximation, and limited conformational sampling. Here, we apply improvements to our sublimation free-energy model with the use of crystal phonon mode calculations to capture the contributions of the vibrational modes of the crystal. Including these improvements with lattice energies computed using the model-potential-based Ψ_mol method leads to accurate estimates of sublimation free energy. Combining these with hydration free energies obtained from either molecular dynamics free-energy perturbation simulations or density functional theory calculations, solubilities comparable to both experiment and informatics predictions are obtained. The application to coronene, succinic acid, and the pharmaceutical desloratadine shows how the methods must be adapted for the adoption of different conformations in different phases. The approach has the flexibility to extend to applications that cannot be covered by informatics methods.

Polymorphic forms of antiandrogenic drug nilutamide: structural and thermodynamic aspects

Attempts to obtain new cocrystals of nonsteroidal antiandrogenic drug nilutamide produced alternative polymorphic forms of the compound (Form II and Form III) and their crystal structures were elucidated by single-crystal X-ray diffraction. Apart from the cocrystallization technique, lyophilization was found to be an effective strategy for achieving polymorph control of nilutamide, which was difficult to obtain by other methods. The physicochemical properties and relative stability of the commercial Form I and newly obtained Form II were comprehensively investigated by a variety of analytical methods (thermal analysis, solution calorimetry, solubility, and sublimation), whereas for Form III, only a handful of experimental parameters were obtained due to the elusive nature of the polymorph. Form I and Form II were found to be monotropically related, with Form I being confirmed as the thermodynamically most stable solid phase. In addition, the performance of different DFT-D and semi-empirical schemes for lattice energy calculation and polymorph energy ranking was compared and analysed. Lattice energy calculations using periodic DFT at B3LYP-D3/6-31(F+)G(d,p) and PBEh-3c/def2-mSVP levels of theory were found to provide the most accurate lattice energy values for Form I against experimental data, while PIXEL and PBEh-3c/def2-mSVP were the only methods that predicted the correct order of stability of Forms I and II.

FFCASP: A Massively Parallel Crystal Structure Prediction Algorithm

A new algorithm called Fast and Flexible CrystAl Structure Predictor (FFCASP) was developed to predict the structure of covalent and molecular crystals. FFCASP is massively parallel and able to handle more than 200 atoms in the unit cell (in other terms, it allows global optimization around 100 individual parameters). It uses a global optimizer specialized for Crystal Structure Prediction (CSP) which combines particle swarm and simulated annealing optimizers. Three different molecular crystals, including diverse intermolecular interactions, namely, cytosine, coumarin, and pyrazinamide, have been selected to evaluate the performance of FFCASP. While cytosine polymorphs have been searched by employing two different force fields (a DFT-SAPT based intermolecular potential and generalized amber force field (GAFF)) up to Z = 16, only GAFF has been used both in coumarin and pyrazinamide polymorph searches up to Z = 4. For these three molecular crystals, FFCASP generated more than 20 000 crystal structures, and the unique ones have been further treated by DFT-D3. A combination of data mining and a machine learning approach was introduced to determine the unique structures and their distribution into different clusters, which ultimately gives an opportunity to retrieve the common features and relations between the resulting structures. There are two known experimental crystal structures of cytosine, and both were successfully located with FFCASP. Two of the reported crystal structures of coumarin have been reproduced. Similarly, in pyrazinamide, three known experimental structures have been rediscovered. In addition to finding the experimentally known structures, FFCASP also located other low-energy structures for each considered molecular crystals. These successes of FFCASP offer the possibility to discover the polymorphic nature of other important molecular crystals (e.g., drugs) as well.

Crystal Structure Prediction Methods for Organic Molecules: State of the Art

The prediction of the crystal structures that a given organic molecule is likely to form is an important theoretical problem of significant interest for the pharmaceutical and agrochemical industries, among others. As evidenced by a series of six blind tests organized over the past 2 decades, methodologies for crystal structure prediction (CSP) have witnessed substantial progress and have now reached a stage of development where they can begin to be applied to systems of practical significance. This article reviews the state of the art in general-purpose methodologies for CSP, placing them within a common framework that highlights both their similarities and their differences. The review discusses specific areas that constitute the main focus of current research efforts toward improving the reliability and widening applicability of these methodologies, and offers some perspectives for the evolution of this technology over the next decade.

Crystal forms in pharmaceutical applications: olanzapine, a gift to crystal chemistry that keeps on giving

This contribution reviews the efforts of many scientists around the world to discover and structurally characterize olanzapine crystal forms, clearing up inconsistencies in the scientific and patent literature and highlighting the challenges in identifying new forms amidst 60+ known polymorphs and solvates. Owing to its remarkable solid-state chemistry, olanzapine has emerged over the last three decades as a popular tool compound for developing new experimental and computational methods for enhanced molecular level understanding of solid-state structure, form diversity and crystallization outcomes. This article highlights the role of olanzapine in advancing the fundamental understanding of crystal forms, interactions within crystal structures, and growth units in molecular crystallization, as well as influencing the way in which drugs are developed today.

Calculation of Diamagnetic Susceptibility Tensors of Organic Crystals: From Coronene to Pharmaceutical Polymorphs

Understanding why crystallization in strong magnetic fields can lead to new polymorphs requires methods to calculate the diamagnetic response of organic molecular crystals. We develop the calculation of the macroscopic diamagnetic susceptibility tensor, χ^cryst, for organic molecular crystals using periodic density functional methods. The crystal magnetic susceptibility tensor, χ^cryst, for all experimentally known polymorphs, and its molecular counterpart, χ^mol, are calculated for flexible pharmaceuticals such as carbamazepine, flufenamic acid, and chalcones, and rigid molecules, such as benzene, pyridine, acridine, anthracene, and coronene, whose molecular magnetic properties have been traditionally studied. A tensor addition method is developed to approximate the crystal diamagnetic susceptibility tensor, χ^cryst, from the molecular one, χ^mol, giving good agreement with those calculated directly using the more costly periodic density functional method for χ^cryst. The response of pharmaceutical molecules and crystals to magnetic fields, as embodied by χ^cryst, is largely determined by the packing in the crystal, as well as the molecular conformation. The anisotropy of χ^cryst can vary considerably between polymorphs though the isotropic terms are fairly constant. The implications for developing a computational method for predicting whether crystallization in a magnetic field could produce a novel or different polymorph are discussed.

Crystal structure determination of an elusive methanol solvate – hydrate of catechin using crystal structure prediction and NMR crystallography

A useful short-cut was developed to limit the number of molecular conformations that need to be regarded in crystal structure prediction calculations, which led to the crystal structure determination of new methanol solvate – hydrate of catechin.

Solubility prediction for a soluble organic molecule via chemical potentials from density of states

While the solubility of a substance is a fundamental property of widespread significance, its prediction from first principles (starting from only the knowledge of the molecular structure of the solute and solvent) remains a challenge. Recently, we proposed a robust and efficient method to predict the solubility from the density of states of a solute-solvent system using classical molecular simulation. The efficiency, and indeed the generality, of the method has now been enhanced by extending it to calculate solution chemical potentials (rather than probability distributions as done previously), from which solubility may be accessed. The method has been employed to predict the chemical potential of Form 1 of urea in both water and methanol for a range of concentrations at ambient conditions and for two charge models. The chemical potential calculations were validated by thermodynamic integration with the two sets of values being in excellent agreement. The solubility determined from the chemical potentials for urea in water ranged from 0.46 to 0.50 mol kg^-1, while that for urea in methanol ranged from 0.62 to 0.85 mol kg^-1, over the temperature range 298-328 K. In common with other recent studies of solubility prediction from molecular simulation, the predicted solubilities differ markedly from experimental values, reflecting limitations of current forcefields.

Mining predicted crystal structure landscapes with high throughput crystallisation: old molecules, new insights

New crystal forms of two well-studied organic molecules are identified in a computationally targeted way, by combining structure prediction with a robotic crystallisation screen, including a ‘hidden’ porous polymorph of trimesic acid.

Organic molecules tend to close pack to form dense structures when they are crystallised from organic solvents. Porous molecular crystals defy this rule: they contain open space, which is typically stabilised by inclusion of solvent in the interconnected pores during crystallisation. The design and discovery of such structures is often challenging and time consuming, in part because it is difficult to predict solvent effects on crystal form stability. Here, we combine crystal structure prediction (CSP) with a robotic crystallisation screen to accelerate the discovery of stable hydrogen-bonded frameworks. We exemplify this strategy by finding new phases of two well-studied molecules in a computationally targeted way. Specifically, we find a new ‘hidden’ porous polymorph of trimesic acid, δ-TMA, that has a guest-free hexagonal pore structure, as well as three new solvent-stabilized diamondoid frameworks of adamantane-1,3,5,7-tetracarboxylic acid (ADTA). Beyond porous solids, this hybrid computational–experimental approach could be applied to a wide range of materials problems, such as organic electronics and drug formulation.

Triptycene End-Capped Quinoxalinophenanthrophenazines (QPPs): Influence of Substituents and Conditions on Aggregation in the Solid State

Triptycene end-capped quinoxalinophenanthrophenazine reveals a coplanar arrangement with a high overlap of the π planes. Four structurally related model compounds bearing electron-withdrawing or -donating groups were synthesized, and their optoelectronic properties were characterized by using cyclovoltammetry, absorption- and emission spectroscopy as well as theoretical calculations. The directional robustness of the triptycene end-capping of these compounds was tested by using single-crystal X-ray diffraction. The impact of solvents and crystallization conditions has also been investigated. In total, 17 single-crystal structures were obtained. Each structure was evaluated for its potential charge-transfer capability taking into account the overall molecular packing, solvent enclathration and the structural overlap of the π planes of adjacent molecules. For this purpose, charge-transfer integrals were also calculated for every π-stacked dimer.

A Million Crystal Structures: The Whole Is Greater than the Sum of Its Parts

The founding in 1965 of what is now called the Cambridge Structural Database (CSD) has reaped dividends in numerous and diverse areas of chemical research. Each of the million or so crystal structures in the database was solved for its own particular reason, but collected together, the structures can be reused to address a multitude of new problems. In this Review, which is focused mainly on the last 10 years, we chronicle the contribution of the CSD to research into molecular geometries, molecular interactions, and molecular assemblies and demonstrate its value in the design of biologically active molecules and the solid forms in which they are delivered. Its potential in other commercially relevant areas is described, including gas storage and delivery, thin films, and (opto)electronics. The CSD also aids the solution of new crystal structures. Because no scientific instrument is without shortcomings, the limitations of CSD research are assessed. We emphasize the importance of maintaining database quality: notwithstanding the arrival of big data and machine learning, it remains perilous to ignore the principle of garbage in, garbage out. Finally, we explain why the CSD must evolve with the world around it to ensure it remains fit for purpose in the years ahead.

A de novo strategy for predictive crystal engineering to tune excitonic coupling

In molecular solids, the intense photoluminescence (PL) observed for solvated dye molecules is often suppressed by nonradiative decay processes introduced by excitonic coupling to adjacent chromophores. We have developed a strategy to avoid this undesirable PL quenching by optimizing the chromophore packing. We integrated the photoactive compounds into metal-organic frameworks (MOFs) and tuned the molecular alignment by introducing adjustable "steric control units" (SCUs). We determined the optimal alignment of core-substituted naphthalenediimides (cNDIs) to yield highly emissive J-aggregates by a computational analysis. Then, we created a large library of handle-equipped MOF chromophoric linkers and computationally screened for the best SCUs. A thorough photophysical characterization confirmed the formation of J-aggregates with bright green emission, with unprecedented photoluminescent quantum yields for crystalline NDI-based materials. This data demonstrates the viability of MOF-based crystal engineering approaches that can be universally applied to tailor the photophysical properties of organic semiconductor materials.

The interplay among molecular structures, crystal symmetries and lattice energy landscapes revealed using unsupervised machine learning: a closer look at pyrrole azaphenacenes

Using unsupervised machine learning and CSPs to help crystallographers better understand how crystallizations are affected by molecular structures.

Accuracy and reproducibility in crystal structure prediction: the curious case of ROY

Because of excessive electron delocalization, the polymorphs of ROY constitute a surprisingly challenging system for crystal structure prediction.

Gibbs free-energy differences between polymorphs via a diabat approach

Polymorph free-energy differences are critical to several applications. A recently proposed diabat interpolation framework estimated free-energy differences between polymorphs by quadratic interpolation of diabats. This work extends the Zwanzig-Bennett relation to the NPT ensemble so that the diabats directly give Gibbs free-energy differences. We also demonstrate how the approach can be used in cases where the diabats are not parabolic. We illustrate the diabat method for Gibbs free-energy difference of zirconium (BCC and HCP phases) and compare it with the conventional lattice switch Monte Carlo approach.

Read between the Molecules: Computational Insights into Organic Semiconductors

The performance and key electronic properties of molecular organic semiconductors are dictated by the interplay between the chemistry of the molecular core and the intermolecular factors of which manipulation has inspired both experimentalists and theorists. This Perspective presents major computational challenges and modern methodological strategies to advance the field. The discussion ranges from insights and design principles at the quantum chemical level, in-depth atomistic modeling based on multiscale protocols, morphological prediction and characterization as well as energy-property maps involving data-driven analysis. A personal overview of the past achievements and future direction is also provided.

Control and prediction of the organic solid state: a challenge to theory and experiment†

The ability of theoretical chemists to quantitatively model the weak forces between organic molecules is being exploited to predict their crystal structures and estimate their physical properties. Evolving crystal structure prediction methods are increasingly being used to aid the design of organic functional materials and provide information about thermodynamically plausible polymorphs of speciality organic materials to aid, for example, pharmaceutical development. However, the increasingly sophisticated experimental studies for detecting the range of organic solid-state behaviours provide many challenges for improving quantitative theories that form the basis for the computer modelling. It is challenging to calculate the relative thermodynamic stability of different organic crystal structures, let alone understand the kinetic effects that determine which polymorphs can be observed and are practically important. However, collaborations between experiment and theory are reaching the stage of devising experiments to target the first crystallization of new polymorphs or create novel organic molecular materials.