Unravelling Guest Dynamics in Crystalline Molecular Organics Using 2H Solid-State NMR and Molecular Dynamics Simulation

2H solid-state NMR and atomistic molecular dynamics (MD) simulations are used to understand the disorder of guest solvent molecules in two cocrystal solvates of the pharmaceutical furosemide. Traditional approaches to interpreting the NMR data fail to provide a coherent model of molecular behavior and indeed give misleading kinetic data. In contrast, the direct prediction of the NMR properties from MD simulation trajectories allows the NMR data to be correctly interpreted in terms of combined jump-type and libration-type motions. Time-independent component analysis of the MD trajectories provides additional insights, particularly for motions that are invisible to NMR. This allows a coherent picture of the dynamics of molecules restricted in molecular-sized cavities to be determined.

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.

Ba6Ge2Se12 and Ba7Ge2Se17: Two Centrosymmetric Barium Seleno-Germanates with Polyatomic Anion Disorder

Herein, the crystal structures and physical properties of two previously unreported barium seleno-germanates, Ba₆Ge₂Se₁₂ and Ba₇Ge₂Se₁₇, are presented. Ba₆Ge₂Se₁₂ adopts the P2₁/c space group with a = 10.0903(2) Å, b = 9.3640(2) Å, c = 25.7643(5) Å, and β = 90.303(1)°, whereas Ba₇Ge₂Se₁₇ crystallizes in the Pnma space group with a = 12.652(1) Å, b = 20.069(2) Å, c = 12.3067(9) Å. Both structures feature polyatomic anion disorder: [Se₂]^2- in the case of Ba₆Ge₂Se₁₂ and [GeSe₅]^4- in the case of Ba₇Ge₂Se₁₇. The anion disorder is verified by comparing pair distribution functions of ordered and disordered models of the structures. These anions are split unevenly across two possible sets of atomic coordinates. The optical band gaps obtained from the powdered samples are found to be 1.75 and 1.51 eV for Ba₆Ge₂Se₁₂ and Ba₇Ge₂Se₁₇, respectively. Differential scanning calorimetry experiments indicate that the compounds are stable under the exclusion of air up to at least 673 K. The thermal diffusivity measurements revealed thermal conductivities reaching values as low as 0.33 W m^-1 K^-1 in both compounds at 573 K.

Combined crystallographic and computational investigation of the solvent disorder present in a new tipiracil hydrochloride methanol solvate–hydrate

We report a computational and crystallographic analysis of tipiracil hydrochloride (methanol solvate–hydrate). It shows a complex network of H-bonds with static discrete disorder in two out of the three symmetry-independent solvent molecules.

Room temperature ferroelectric copper(ii) coordination polymers based on amino acid hydrazide ligands

This work brings into focus the superior coordination properties and facile applicability of amino acid hydrazide ligands for the design of molecular magnets and molecular ferroelectrics.

Analyzing Dynamical Disorder for Charge Transport in Organic Semiconductors via Machine Learning

Organic semiconductors are indispensable for today's display technologies in the form of organic light-emitting diodes (OLEDs) and further optoelectronic applications. However, organic materials do not reach the same charge carrier mobility as inorganic semiconductors, limiting the efficiency of devices. To find or even design new organic semiconductors with higher charge carrier mobility, computational approaches, in particular multiscale models, are becoming increasingly important. However, such models are computationally very costly, especially when large systems and long timescales are required, which is the case to compute static and dynamic energy disorder, i.e., the dominant factor to determine charge transport. Here, we overcome this drawback by integrating machine learning models into multiscale simulations. This allows us to obtain unprecedented insight into relevant microscopic materials properties, in particular static and dynamic disorder contributions for a series of application-relevant molecules. We find that static disorder and thus the distribution of shallow traps are highly asymmetrical for many materials, impacting widely considered Gaussian disorder models. We furthermore analyze characteristic energy level fluctuation times and compare them to typical hopping rates to evaluate the importance of dynamic disorder for charge transport. We hope that our findings will significantly improve the accuracy of computational methods used to predict application-relevant materials properties of organic semiconductors and thus make these methods applicable for virtual materials design.

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.

Raman and Terahertz Spectroscopic Characterization of Solid-state Cocrystal Formation within Specific Active Pharmaceutical Ingredients

Cocrystallization of specific active pharmaceutical ingredients (APIs) in the solid-state phase is becoming a feasible way to improve their corresponding physicochemical properties and ultimate bioavailability without making and breaking any covalent bonds within them. Many recent reports deal with the characterization and analysis topics of pharmaceutical APIs-based cocrystals. In this mini-review, we will focus on the recent steady-state and time-dependent spectroscopic investigation into the cocrystallization of specific APIs based on both Raman and emerging terahertz spectroscopy in pharmaceutical fields. Distinctive spectral, structural and also kinetic information of pharmaceutical APIs-based cocrystals are obtained and discussed, which would highlight the potential of vibrational spectroscopy as an attractive technique for various drug research and development during cocrystallization of specific APIs.

On the entropy cost of making solvates

We present a simple way of estimating the entropy cost of solvate formation in crystals. The entropy penalty of making solvates can be as low as <1 kJ mol^-1 or as high as >9 kJ mol^-1 and is entirely dependent on the nature of the liquid component and the temperature of solvate formation. A link is found between a low entropy cost and a higher likelihood for a solvent to make solvates.

Same or different – that is the question: identification of crystal forms from crystal structure data

An analysis of the CSD with structural comparison tools shows that differentiating between polymorphism and redeterminations is not always straight forward and requires of complementary tools at the hands of an expert practitioner.

The importance of configurational disorder in crystal structure prediction: the case of loratadine

Loratadine, an over-the-counter antihistamine medication, has two known monotropically related polymorphs, both of which feature disorder. A combined experimental and computational approach using variable temperature single crystal X-ray diffraction (VT-SCXRD) analysis and dispersion corrected density functional theory (DFT-D) reveals that the nature of the disorder in each form is markedly different and cannot be described by a simple isolated-site model with thermally populated conformations in either of the two cases. In Form I, the ethyl carbamate functionality adopts two different configurations, with adjacent moieties interacting along one-dimensional chains. The most stable arrangement features alternating configurations, but because of the low energetic cost of stacking faults, the domain sizes are short and an average crystal structure is observed experimentally. The configurational free energy of the disordered structure is lower than the energy of the two corresponding ordered crystal structures, but the energy decrease is dominated by the lower lattice energy of the alternating arrangement with a small entropic contribution. In Form II, the flexible cycloheptane bridge adopts two different configurations. The disorder is not an equilibrium property but is instead frozen-in during the crystallisation process. The configurational free energy of the disordered structure falls in between the lattice energies of the two corresponding ordered structures. The two ordered components of each disordered structure are all found in a crystal structure prediction (CSP) study with the GRACE programme. However, the experimentally observed stability relationship is only reproduced when the energy contribution of disorder is taken into account. The disordered model of Form I is found to be lower in energy than all the other predicted structures and there is no indication of a missing, thermodynamically more stable, form. The case of loratadine demonstrates that experimentally observed disorder close to 50/50 does not necessarily correspond to a free energy decrease by kT ln 2.

Screening for new pharmaceutical solid forms using mechanochemistry: A practical guide

Within the pharmaceutical industry, and elsewhere, the screening for new solid forms is a mandatory exercise for both existing and new chemical entities. This contribution focuses on mechanochemistry as a versatile approach for discovering new and alternative solid forms. Whilst a series of recently published extensive reviews exist which focus on mechanistic aspects and potential areas of development, in this review we focus on particular practical aspects of mechanochemistry in order to allow full optimisation of the approach in searches for new solid forms including polymorphs, salts and cocrystals as well as their solvated/hydrated analogues. As a consequence of the apparent experimental simplicity of the method (compared to more traditional protocols e.g. solvent-based methods), the high efficiency and range of conditions available in a mechanochemical screen, mechanochemistry should not be considered simply as an alternative method when other screening methods are not successful, but rather as a key strategy in any fully effective solid form screen providing reduced effort and time as well as the potential of requiring reduced amounts of material.

Guest control of structure in porous organic cages

Two porous organic cages with different thermodynamic polymorphs were induced by co-solvents to interchange their crystal packing modes, thus achieving guest-mediated control over solid-state porosity. In situ crystallography allows the effect of the co-solvent guests on these structural interconversions to be understood.

Combined crystal structure prediction and high-pressure crystallization in rational pharmaceutical polymorph screening

Organic molecules, such as pharmaceuticals, agro-chemicals and pigments, frequently form several crystal polymorphs with different physicochemical properties. Finding polymorphs has long been a purely experimental game of trial-and-error. Here we utilize in silico polymorph screening in combination with rationally planned crystallization experiments to study the polymorphism of the pharmaceutical compound Dalcetrapib, with 10 torsional degrees of freedom one of the most flexible molecules ever studied computationally. The experimental crystal polymorphs are found at the bottom of the calculated lattice energy landscape, and two predicted structures are identified as candidates for a missing, thermodynamically more stable polymorph. Pressure-dependent stability calculations suggested high pressure as a means to bring these polymorphs into existence. Subsequently, one of them could indeed be crystallized in the 0.02 to 0.50 GPa pressure range and was found to be metastable at ambient pressure, effectively derisking the appearance of a more stable polymorph during late-stage development of Dalcetrapib.

Crystal polymorphism can lead to substances with vastly differing physicochemical properties, which has serious implications in the pharmaceutical industry. Here, the authors use in silico polymorph screening to accurately predict the resulting structures under set crystallisation environments.

Static and lattice vibrational energy differences between polymorphs

Lattice energy, entropy and free energy differences for over 500 pairs of known polymorphs are computed and discussed.

Why don't we find more polymorphs?

Crystal structure prediction (CSP) studies are not limited to being a search for the most thermodynamically stable crystal structure, but play a valuable role in understanding polymorphism, as shown by interdisciplinary studies where the crystal energy landscape has been explored experimentally and computationally. CSP usually produces more thermodynamically plausible crystal structures than known polymorphs. This article illustrates some reasons why: because (i) of approximations in the calculations, particularly the neglect of thermal effects (see §1.1); (ii) of the molecular rearrangement during nucleation and growth (see §1.2); (iii) the solid-state structures observed show dynamic or static disorder, stacking faults, other defects or are not crystalline and so represent more than one calculated structure (see §1.3); (iv) the structures are metastable relative to other molecular compositions (see §1.4); (v) the right crystallization experiment has not yet been performed (see §1.5) or (vi) cannot be performed (see §1.6) and the possibility (vii) that the polymorphs are not detected or structurally characterized (see §1.7). Thus, we can only aspire to a general predictive theory for polymorphism, as this appears to require a quantitative understanding of the kinetic factors involved in all possible multi-component crystallizations. For a specific molecule, analysis of the crystal energy landscape shows the potential complexity of its crystallization behaviour.