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

How Accurate Are Simulations and Experiments for the Lattice Energies of Molecular Crystals?

Molecular crystals play a central role in a wide range of scientific fields, including pharmaceuticals and organic semiconductor devices. However, they are challenging systems to model accurately with computational approaches because of a delicate interplay of intermolecular interactions such as hydrogen bonding and Van der Waals dispersion forces. Here, by exploiting recent algorithmic developments, we report the first set of diffusion Monte Carlo lattice energies for all 23 molecular crystals in the popular and widely used X23 dataset. Comparisons with previous state-of-the-art lattice energy predictions (on a subset of the dataset) and a careful analysis of experimental sublimation enthalpies reveals that high-accuracy computational methods are now at least as reliable as (computationally derived) experiments for the lattice energies of molecular crystals. Overall, this work demonstrates the feasibility of high-level explicitly correlated electronic structure methods for broad benchmarking studies in complex condensed phase systems, and signposts a route towards closer agreement between experiment and simulation.

Machine Learning Nucleation Collective Variables with Graph Neural Networks

The efficient calculation of nucleation collective variables (CVs) is one of the main limitations to the application of enhanced sampling methods to the investigation of nucleation processes in realistic environments. Here we discuss the development of a graph-based model for the approximation of nucleation CVs that enables orders-of-magnitude gains in computational efficiency in the on-the-fly evaluation of nucleation CVs. By performing simulations on a nucleating colloidal system mimicking a multistep nucleation process from solution, we assess the model's efficiency in both postprocessing and on-the-fly biasing of nucleation trajectories with pulling, umbrella sampling, and metadynamics simulations. Moreover, we probe and discuss the transferability of graph-based models of nucleation CVs across systems using the model of a CV based on sixth-order Steinhardt parameters trained on a colloidal system to drive the nucleation of crystalline copper from its melt. Our approach is general and potentially transferable to more complex systems as well as to different CVs.

Hybrid and composite materials of organic crystals

Organic molecular crystals have historically been viewed as delicate and fragile materials. However, recent studies have revealed that many organic crystals, especially those with high aspect ratios, can display significant flexibility, elasticity, and shape adaptability. The discovery of mechanical compliance in organic crystals has recently enabled their integration with responsive polymers and other components to create novel hybrid and composite materials. These hybrids exhibit unique structure-property relationships and synergistic effects that not only combine, but occasionally also enhance the advantages of the constituent crystals and polymers. Such organic crystal composites rapidly emerge as a promising new class of materials for diverse applications in optics, electronics, sensing, soft robotics, and beyond. While specific, mostly practical challenges remain regarding scalability and manufacturability, being endowed with both structurally ordered and disordered components, the crystal-polymer composite materials set a hitherto unexplored yet very promising platform for the next-generation adaptive devices. This Perspective provides an in-depth analysis of the state-of-the-art in design strategies, dynamic properties and applications of hybrid and composite materials centered on organic crystals. It addresses the current challenges and provides a future outlook on this emerging class of multifunctional, stimuli-responsive, and mechanically robust class of materials.

Benchmark Data Set of Crystalline Organic Semiconductors

This work reports a Benchmark Data set of Crystalline Organic Semiconductors to test calculations of the structural and electronic properties of these materials in the solid state. The data set contains 67 crystals consisting of mostly rigid molecules with a single dominant conformer, covering the majority of known structural types. The experimental crystal structure is available for the entire data set, whereas zero-temperature unit cell volume can be reliably estimated for a subset of 28 crystals. Using this subset, we benchmark r2SCAN-D3 and PBE-D3 density functionals. Then, for the entire data set, we benchmark approximate density functional theory (DFT) methods, including GFN1-xTB and DFTB3(3ob-3-1), with various dispersion corrections against r2SCAN-D3. Our results show that r2SCAN-D3 geometries are accurate within a few percent, which is comparable to the statistical uncertainty of experimental data at a fixed temperature, but the unit cell volume is systematically underestimated by 2% on average. The several times faster PBE-D3 provides an unbiased estimate of the volume for all systems except for molecules with highly polar bonds, for which the volume is substantially overestimated in correlation with the underestimation of atomic charges. Considered approximate DFT methods are orders of magnitude faster and provide qualitatively correct but overcompressed crystal structures unless the dispersion corrections are fitted by unit cell volume.

Self-assembly controlled at the level of individual functional groups

Molecular self-assembly is driven by intermolecular interactions between the functional groups on the component molecules. Small changes in molecular structure can make large differences in extended structure, and understanding this connection will lead to predictive power and control of the self-assembly process. Scanning tunneling microscopy is used to study self-assembly in two-dimensional clusters and monolayers, and the experimental approach is to study "families" of molecules where one or more functional groups is varied in a methodical way. Studied families include indole carboxylic acids, isatin derivatives (which have the indole backbone), quinaldic acid, thioethers, and fluorenone derivatives. In these systems, a variety of intermolecular interactions drive the assembly of the molecular monolayer, including hydrogen bonds, van der Waals forces, zwitterionic interactions, surface interactions, and halogen interactions.

Minimal Basis Set Hartree-Fock Corrected with Atom-Centered Potentials for Molecular Crystal Modeling and Crystal Structure Prediction

Crystal structure prediction (CSP), determining the experimentally observable structure of a molecular crystal from the molecular diagram, is an important challenge with technologically relevant applications in materials manufacturing and drug design. For the purpose of screening the randomly generated candidate crystal structures, CSP protocols require energy ranking methods that are fast and can accurately capture the small energy differences between molecular crystals. In addition, a good ranking method should also produce accurate equilibrium geometries, both intramolecular and intermolecular. In this article, we explore the combination of minimal-basis-set Hartree-Fock (HF) with atom-centered potentials (ACPs) as a method for modeling the structure and energetics of molecular crystals. The ACPs are developed for the H, C, N, and O atoms and fitted to a set of reference data at the B86bPBE-XDM level in order to mitigate basis-set incompleteness and missing correlation. In particular, ACPs are developed in combination with two methods: HF-D3/MINIs and HF-3c. The application of ACPs greatly improves the performance of HF-D3/MINIs for lattice energies, crystal energy differences, energy-volume and energy-strain relations, and crystal geometries. In the case of HF-3c, the improvement in the crystal energy differences is much smaller than in HF-D3/MINIs, but lattice energies and particularly crystal geometries are considerably better when ACPs are used. The resulting methods may be useful for CSP but also for quick calculation of molecular crystal lattice energies and geometries.

Efficient Screening of Coformers for Active Pharmaceutical Ingredient Cocrystallization

Controlling the physical properties of solid forms for active pharmaceutical ingredients (APIs) through cocrystallization is an important part of drug product development. However, it is difficult to know a priori which coformers will form cocrystals with a given API, and the current state-of-the-art for cocrystal discovery involves an expensive, time-consuming, and, at the early stages of pharmaceutical development, API material-limited experimental screen. We propose a systematic, high-throughput computational approach primarily aimed at identifying API/coformer pairs that are unlikely to lead to experimentally observable cocrystals and can therefore be eliminated with only a brief experimental check, from any experimental investigation. On the basis of a well-established crystal structure prediction (CSP) methodology, the proposed approach derives its efficiency by not requiring any expensive quantum mechanical calculations beyond those already performed for the CSP investigation of the neat API itself. The approach and assumptions are tested through a computational investigation on 30 potential 1:1 multicomponent systems (cocrystals and solvate) involving 3 active pharmaceutical ingredients and 9 coformers and one solvent. This is complemented with a detailed experimental investigation of all 30 pairs, which led to the discovery of five new cocrystals (three API-coformer combinations, a polymorphic cocrystal example, and one with different stoichiometries) and a cis-aconitic acid polymorph. The computational approach indicates that, for some APIs, a significant proportion of all potential API/coformer pairs could be investigated with only a brief experimental check, thereby saving considerable experimental effort.

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.

Emerging Landscape of Computational Modeling in Pharmaceutical Development

Computational chemistry applications have become an integral part of the drug discovery workflow over the past 35 years. However, computational modeling in support of drug development has remained a relatively uncharted territory for a significant part of both academic and industrial communities. This review considers the computational modeling workflows for three key components of drug preclinical and clinical development, namely, process chemistry, analytical research and development, as well as drug product and formulation development. An overview of the computational support for each step of the respective workflows is presented. Additionally, in context of solid form design, special consideration is given to modern physics-based virtual screening methods. This covers rational approaches to polymorph, coformer, counterion, and solvent virtual screening in support of solid form selection and design.

Accessing a regiospecific isomer and a metastable polymorph through crystal engineering and solid-state reaction

We describe a solid-state Diels–Alder reaction where crystal engineering was used to design a reaction site yielding one regioisomer. Reaction was followed with SCXRD, compared to solution synthesis and rationalised using computational modelling.

Chemical reactivity prediction: current methods and different application areas

The ability to predict chemical reactivity of a molecule is highly desirable in drug discovery, both ex vivo (synthetic route planning, formulation, stability) and in vivo: metabolic reactions determine pharmacodynamics, pharmacokinetics and potential toxic effects, and early assessment of liabilities is vital to reduce attrition rates in later stages of development. Quantum mechanics offer a precise description of the interactions between electrons and orbitals in the breaking and forming of new bonds. Modern algorithms and faster computers have allowed the study of more complex systems in a punctual and accurate fashion, and answers for chemical questions around stability and reactivity can now be provided. Through machine learning, predictive models can be built out of descriptors derived from quantum mechanics and cheminformatics, even in the absence of experimental data to train on. In this article, current progress on computational reactivity prediction is reviewed: applications to problems in drug design, such as modelling of metabolism and covalent inhibition, are highlighted and unmet challenges are posed.

Solid-State NMR-Driven Crystal Structure Prediction of Molecular Crystals: The Case of Mebendazole

Among all possible NMR crystallography approaches for crystal-structure determination, crystal structure prediction - NMR crystallography (CSP-NMRX) has recently turned out to be a powerful method. In the latter, the original procedure exploited solid-state NMR (SSNMR) information during the final steps of the prediction. In particular, it used the comparison of computed and experimental chemical shifts for the selection of the correct crystal packing. Still, the prediction procedure, generally carried out with DFT methods, may require important computational resources and be quite time-consuming, especially if there are no available constraints to use at the initial stage. Herein, the successful application of this combined prediction method, which exploits NMR information also in the input step to reduce the search space of the predictive algorithm, is presented. Herein, this method was applied on mebendazole, which is characterized by desmotropism. The use of SSNMR data as constraints for the selection of the right tautomer and the determination of the number of independent molecules in the unit cell led to a considerably faster process, reducing the number of calculations to be performed. In this way, the crystal packing was successfully predicted for the three known phases of mebendazole. To evaluate the quality of the predicted structures, these were compared to the experimental ones. The crystal structure of phase B of mebendazole, in particular, was determined de novo by powder diffraction and is presented for the first time in this paper.

Accurate and Efficient Computation of Optical Absorption Spectra of Molecular Crystals: The Case of the Polymorphs of ROY

When calculating the optical absorption spectra of molecular crystals from first principles, the influence of the crystalline environment on the excitations is of significant importance. For such systems, however, methods to describe the excitations accurately can be computationally prohibitive due to the relatively large system sizes involved. In this work, we demonstrate a method that allows optical absorption spectra to be computed both efficiently and at high accuracy. Our approach is based on the spectral warping method successfully applied to molecules in solvent. It involves calculating the absorption spectrum of a supercell of the full molecular crystal using semi-local time-dependent density functional theory (TDDFT), before warping the spectrum using a transformation derived from smaller-scale semi-local and hybrid TDDFT calculations on isolated dimers. We demonstrate the power of this method on three polymorphs of the well-known color polymorphic compound ROY and find that it outperforms both small-scale hybrid TDDFT dimer calculations and large-scale semi-local TDDFT supercell calculations, when compared to the experiment.

Bright Frenkel Excitons in Molecular Crystals: A Survey

We computed the optical properties of a large set of molecular crystals (∼2200 structures) composed of molecules whose lowest excited states are strongly coupled and generate wide excitonic bands. Such bands are classified in terms of their dimensionality (1-, 2-, and 3-dimensional), the position of the optically allowed state in relation with the excitonic density of states, and the presence of Davydov splitting. The survey confirms that one-dimensional aggregates are rare in molecular crystals highlighting the need to go beyond the simple low-dimensional models. Furthermore, this large set of data is used to search for technologically interesting and less common properties. For instance, we considered the largest excitonic bandwidth that is achievable within known molecular crystals and identified materials with strong super-radiant states. Finally, we explored the possibility that strong excitonic coupling can be used to generate emissive states in the near-infrared region in materials formed by molecules with bright visible absorption and we could identify the maximum allowable red shift in this material class. These insights with the associated searchable database provide practical guidelines for designing materials with interesting optical properties.

Predicting Energetics Materials' Crystalline Density from Chemical Structure by Machine Learning

To expedite new molecular compound development, a long-sought goal within the chemistry community has been to predict molecules' bulk properties of interest a priori to synthesis from a chemical structure alone. In this work, we demonstrate that machine learning methods can indeed be used to directly learn the relationship between chemical structures and bulk crystalline properties of molecules, even in the absence of any crystal structure information or quantum mechanical calculations. We focus specifically on a class of organic compounds categorized as energetic materials called high explosives (HE) and predicting their crystalline density. An ongoing challenge within the chemistry machine learning community is deciding how best to featurize molecules as inputs into machine learning models-whether expert handcrafted features or learned molecular representations via graph-based neural network models-yield better results and why. We evaluate both types of representations in combination with a number of machine learning models to predict the crystalline densities of HE-like molecules curated from the Cambridge Structural Database, and we report the performance and pros and cons of our methods. Our message passing neural network (MPNN) based models with learned molecular representations generally perform best, outperforming current state-of-the-art methods at predicting crystalline density and performing well even when testing on a data set not representative of the training data. However, these models are traditionally considered black boxes and less easily interpretable. To address this common challenge, we also provide a comparison analysis between our MPNN-based model and models with fixed feature representations that provides insights as to what features are learned by the MPNN to accurately predict density.

A non-empirical intermolecular force-field for trinitrobenzene and its application in crystal structure prediction

An anisotropic atom-atom distributed intermolecular force-field (DIFF) for rigid trinitrobenzene (TNB) is developed using distributed multipole moments, dipolar polarizabilities, and dispersion coefficients derived from the charge density of the isolated molecule. The short-range parameters of the force-field are fitted to first- and second-order symmetry-adapted perturbation theory dimer interaction energy calculations using the distributed density-overlap model to guide the parameterization of the short-range anisotropy. The second-order calculations are used for fitting the damping coefficients of the long-range dispersion and polarization and also for relaxing the isotropic short-range coefficients in the final model, DIFF-srL2(rel). We assess the accuracy of the unrelaxed model, DIFF-srL2(norel), and its equivalent without short-range anisotropy, DIFF-srL0(norel), as these models are easier to derive. The model potentials are contrasted with empirical models for the repulsion-dispersion fitted to organic crystal structures with multipoles of iterated stockholder atoms (ISAs), FIT(ISA,L4), and with Gaussian Distributed Analysis (GDMA) multipoles, FIT(GDMA,L4), commonly used in modeling organic crystals. The potentials are tested for their ability to model the solid state of TNB. The non-empirical models provide more reasonable relative lattice energies of the three polymorphs of TNB and propose more sensible hypothetical structures than the empirical force-field (FIT). The DIFF-srL2(rel) model successfully has the most stable structure as one of the many structures that match the coordination sphere of form III. The neglect of the conformational flexibility of the nitro-groups is a significant approximation. This methodology provides a step toward force-fields capable of representing all phases of a molecule in molecular dynamics simulations.

A strategy for predicting the crystal structure of energetic N-oxides based on molecular similarity and electrostatic matching

A strategy for crystal structure prediction of high energy materials was proposed based on “homologous crystals”.

Phosphangulene: A Molecule for All Chemists

Phosphangulene (1) is a hexacyclic triarylphosphine with a distinctive conical shape and other features that allow the compound to be viewed from diverse perspectives and to be embraced by chemists from different parts of the field as a molecule worthy of special attention. In recent work, phosphangulene and its derivatives have proven to be effective tools for probing general principles that govern molecular organization in solids. The phosphangulene family is particularly well-suited for these studies because systematic structural changes in the compounds are easy to introduce. In crystals of phosphangulene itself, molecules are stacked efficiently like hats, giving rise to an R3m structure that is polar and pyroelectric. Simple conversion of the compound into phosphangulene oxide (7a) or other chalcogenides blocks effective stacking and forces crystallization to produce alternative structures that have many suboptimal intermolecular interactions and vary little in energy as their geometries are altered. This leads to high levels of polymorphism, and phosphangulene oxide (7a) belongs to the elite set of compounds known to exist in five or more forms characterized by single-crystal X-ray diffraction. For similar reasons, phosphangulene chalcogenides form crystals with complex unit cells in which multiple inequivalent molecules are needed to optimize packing, and the compounds are also predisposed to form solvates and mixed crystals containing other molecules. For example, crystallization of a 1:1 mixture of phosphangulene and oxide 7a yielded needles composed of pure phosphangulene along with crystals of the oxide containing substantial amounts of phosphangulene. Phosphangulene has one known polymorph, and its crystallization rejects the oxide. In contrast, the oxide is highly polymorphic, and its crystallization is prone to errors in which molecules in the lattice are replaced by other compounds. Packing in crystals of the oxide appears to be so ineffective that the orientation and even the identity of the molecular components can be varied without imposing severe energetic penalties.Because substituted members of the phosphangulene family have awkward curved shapes that cannot be packed efficiently, they have emerged as highly effective partners for cocrystallizing fullerenes and for using concave-convex interactions to control how fullerenes can be organized in materials. This can be achieved without eliminating fullerene-fullerene contacts of the type needed to ensure conductivity. In addition, phosphangulene has created unlimited opportunities for making complex structures with large curved aromatic surfaces based on a new strategy in which the central atom of phosphorus is used to form covalent bonds with other elements or to introduce coordinative interactions with metals. In these ways, recent work has put phosphangulene in the spotlight as a compound of unusually broad interest and shown that it can appropriately be called a molecule for all chemists.

ROY Reclaims Its Crown: New Ways To Increase Polymorphic Diversity

Chemical compounds that exist in multiple crystalline forms are said to exhibit polymorphism. Polymorphs have the same composition, but their structures and properties can vary markedly. In many fields, conditions for crystallizing compounds of interest are screened exhaustively to generate as many polymorphs as possible, from which the most advantageous form can be selected. We report new ways to search for polymorphs and increase polymorphic diversity, based on crystallization induced by suitably designed mixed-crystal seeds. The potential of the strategy has been demonstrated by using it to produce new polymorphs of the benchmark compound ROY as single crystals structurally characterized by X-ray diffraction. This allows ROY to reclaim its crown as the most polymorphic compound in the Cambridge Structural Database. More generally, the methods promise to become valuable tools for polymorphic screening in all fields where crystalline solids are used.

Interplay between Polymorphism and Isostructurality in the 2-Fur- and 2-Thenaldehyde Semi- and Thiosemicarbazones

The four compounds, namely: 5-nitro-2-furaldehyde thiosemicarbazone (1); 5-nitro-2-thiophene thiosemicarbazone (2); 5-nitro-2-furaldehyde semicarbazone (3); and 5-nitro-2-thiophene semicarbazone (4) were synthesized and crystallized. The three new crystal structures of 1, 2, and 4 were determined and compared to three already known crystal structures of 3. Additionally, two new polymorphic forms of 1 solvate were synthesized and studied. The influence of the exchange of 2-thiophene to 2-furaldehyde as well as thiosemicarbazone and semicarbazone on the self-assembly of supramolecular nets was elucidated and discussed in terms of the formed synthons and assemblies accompanied by Full Interaction Maps analysis. Changes in the strength of IR oscillators caused by the molecular and crystal packing effects are described and explained in terms of changes of electron density.

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.

Charge mobility calculation of organic semiconductors without use of experimental single-crystal data

Prediction of material properties of newly designed molecules is a long-term goal in organic electronics. In general, it is a difficult problem, because the material properties are dominated by the unknown packing structure. We present a practical method to obtain charge transport properties of organic single crystals, without use of experimental single-crystal data. As a demonstration, we employ the promising molecule C10-DNBDT. We succeeded in quantitative evaluation of charge mobility of the single crystal using our quantum wave-packet dynamical simulation method. Here, the single-crystal data is computationally obtained by searching possible packing structures from structural formula of the molecule. We increase accuracy in identifying the actual crystal structure from suggested ones by using not only crystal energy but also similarity between calculated and experimental powder X-ray diffraction patterns. The proposed methodology can be a theoretical design technique for efficiently developing new high-performance organic semiconductors, since it can estimate the charge transport properties at early stage in the process of material development.

Lab in a DAC - high-pressure crystal chemistry in a diamond-anvil cell

The diamond-anvil cell (DAC) was invented 60 years ago, ushering in a new era for material sciences, extending research into the dimension of pressure. Most structural determinations and chemical research have been conducted at ambient pressure, i.e. the atmospheric pressure on Earth. However, modern experimental techniques are capable of generating pressure and temperature higher than those at the centre of Earth. Such extreme conditions can be used for obtaining unprecedented chemical compounds, but, most importantly, all fundamental phenomena can be viewed and understood from a broader perspective. This knowledge, in turn, is necessary for designing new generations of materials and applications, for example in the pharmaceutical industry or for obtaining super-hard materials. The high-pressure chambers in the DAC are already used for a considerable variety of experiments, such as chemical reactions, crystallizations, measurements of electric, dielectric and magnetic properties, transformations of biological materials as well as experiments on living tissue. Undoubtedly, more applications involving elevated pressure will follow. High-pressure methods become increasingly attractive, because they can reduce the sample volume and compress the intermolecular contacts to values unattainable by other methods, many times stronger than at low temperature. The compressed materials reveal new information about intermolecular interactions and new phases of single- and multi-component compounds can be obtained. At the same time, high-pressure techniques, and particularly those of X-ray diffraction using the DAC, have been considerably improved and many innovative developments implemented. Increasingly more equipment of in-house laboratories, as well as the instrumentation of beamlines at synchrotrons and thermal neutron sources are dedicated to high-pressure research.

Crystal Structure Prediction via Basin-Hopping Global Optimization Employing Tiny Periodic Simulation Cells, with Application to Water-Ice

A crystal structure prediction algorithm for use in periodic boundary conditions with empirical rigid models is presented, which employs (i) unrestricted cutoff radii for the real-space interactions, thus allowing the treatment of even very small unit cells, and (ii) a global-optimization algorithm based on the basin-hopping method of Wales et al. (D. J. Wales and J. P. K. Doye, J. Phys. Chem. A 1997, 101, 5111). The algorithm is then applied to the TIP4P model of water (W. L. Jorgensen et al., J. Chem. Phys. 1983, 79, 926.) in order to find the lowest enthalpy water-ice crystalline structures in the pressure region 0-8000 bar, in unit cells holding in the range of 1-16 molecules, and a database of the 10 lowest enthalpy structures found at pressures 0, 4000, and 8000 bar is presented. The algorithm finds many of the ice polymorphs and, in particular, finds that the lowest energy structure at zero pressure is almost exactly tied between an ice Ic (cubic ice) and ice Ih (hexagonal ice) structure, having near-identical energies.

The role of molecular modelling and simulation in the discovery and deployment of metal-organic frameworks for gas storage and separation

Metal-organic frameworks (MOFs) are highly tuneable, extended-network, crystalline, nanoporous materials with applications in gas storage, separations, and sensing. We review how molecular models and simulations of gas adsorption in MOFs have informed the discovery of performant MOFs for methane, hydrogen, and oxygen storage, xenon, carbon dioxide, and chemical warfare agent capture, and xylene enrichment. Particularly, we highlight how large, open databases of MOF crystal structures, post-processed to enable molecular simulations, are a platform for computational materials discovery. We discuss how to orient research efforts to routinise the computational discovery of MOFs for adsorption-based engineering applications.

The solid state forms of the sex hormone 17-β-estradiol

The crystal structure of the female sex hormone has been established despite its high affinity for water.