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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., (open access.)

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., (open access.)

In this paper, we report the use of an NIR fiber-optic spectrometer with a high-speed diode array for calibration-free monitoring and modeling of the reaction of acetic anhydride with butanol using the catalyst 4-(dimethylamino)pyridine in a microscale batch reactor. Acquisition of spectra at 5 ms/scan gave information relevant for modeling these fast batch processes with a single multibatch kinetic model. Nonlinear fitting of a first-principles model directly to the reaction spectra gave calibration-free estimates of time-dependent concentration profiles and pure component spectra. The amount of catalyst was varied between different batches to permit accurate estimation of its effect in the multiway model. A wide range of different models with increasing complexity could be fit to each batch individually with low residuals and apparent low lack of fit. However, only one model properly estimated the concentration profiles when all five batches were fitted simultaneously in a multiway kinetic model. Inclusion of on-line temperature measurements and use of an Arrhenius model for the estimated rate constant gave significantly improved model fits compared to an isothermal kinetic model. Augmentation of prerun batches with data from an additional batch permitted model-based forecasts of reaction trajectories, reaction yield, reaction end points, and process upsets. One batch with added water to simulate a process upset was easily detected by the calibration free process model.