Faraday Discussions最新文献

A nearly universal component of NMR crystallography is the ranking of candidate structures based on how well their first-principles-predicted NMR parameters align with the results of solid-state NMR experiments. Here, a novel approach for assigning probabilities to candidate models is proposed that quantifies the likelihood that each model is the correct experimental structure. This method employs hierarchical Bayesian inference and leverages explicit prior probabilities derived from a uniform distribution of potential candidate structures with respect to chi-squared values. The resulting uniform chi-squared (UC) model provides a more cautious estimate of candidate probabilities compared to previous approaches, assigning decreased likelihood to the best-fit structure and increased likelihood to alternate candidates. Although developed here within the context of NMR crystallography, the UC model represents a general method for assigning likelihoods based on chi-squared goodness-of-fit assessments.

Active Thermochemical Tables (ATcT) were successfully used to resolve the existing inconsistencies related to the thermochemistry of glycine, based on statistically analyzing and solving a thermochemical network that includes >3350 chemical species interconnected by nearly 35 000 thermochemically-relevant determinations from experiment and high-level theory. The current ATcT results for the 298.15 K enthalpies of formation are −394.70 ± 0.55 kJ mol⁻¹ for gas phase glycine, −528.37 ± 0.20 kJ mol⁻¹ for solid α-glycine, −528.05 ± 0.22 kJ mol⁻¹ for β-glycine, −528.64 ± 0.23 kJ mol⁻¹ for γ-glycine, −514.22 ± 0.20 kJ mol⁻¹ for aqueous undissociated glycine, and −470.09 ± 0.20 kJ mol⁻¹ for fully dissociated aqueous glycine at infinite dilution. In addition, a new set of thermophysical properties of gas phase glycine was obtained from a fully corrected nonrigid rotor anharmonic oscillator (NRRAO) partition function, which includes all conformers. Corresponding sets of thermophysical properties of α-, β-, and γ-glycine are also presented.

Organic molecular crystals exhibit various functions due to their diverse molecular structures and arrangements. Computational approaches are necessary to explore novel molecular crystals from the material space, but quantum chemical calculations are costly and time-consuming. Neural network potentials (NNPs), trained on vast amounts of data, have recently gained attention for their ability to perform energy calculations with accuracy comparable to quantum chemical methods at high speed. However, NNPs trained on datasets primarily consisting of inorganic crystals, such as the Materials Project, may introduce bias when applied to organic molecular crystals. This study investigates the strategies to improve the accuracy of a pre-trained NNP for organic molecular crystals by distilling knowledge from a teacher model. The most effective knowledge transfer was achieved when fine-tuning using only soft targets, i.e., the teacher model's inference values. As the ratio of hard target loss increased, the efficiency of knowledge transfer decreased, leading to overfitting. As a proof of concept, the NNP created through knowledge distillation was used to predict elastic properties, resulting in improved accuracy compared to the pre-trained model.

The recent development of ultra-fast magic-angle spinning (MAS) (>100 kHz) provides new opportunities for structural characterization in solids. Here, we use NMR crystallography to validate the structure of verinurad, a microcrystalline active pharmaceutical ingredient. To do this, we take advantage of ¹H resolution improvement at ultra-fast MAS and use solely ¹H-detected experiments and machine learning methods to assign all the experimental proton and carbon chemical shifts. This framework provides a new tool for elucidating chemical information from crystalline samples with limited sample volume and yields remarkably faster acquisition times compared to ¹³C-detected experiments, without the need to employ dynamic nuclear polarization.

We determine the complete atomic-level structure of the amorphous form of the drug atuliflapon, a 5-lipooxygenase activating protein (FLAP) inhibitor, via chemical-shift-driven NMR crystallography. The ensemble of preferred structures allows us to identify a number of specific conformations and interactions that stabilize the amorphous structure. These include preferred hydrogen-bonding motifs with water and with other drug molecules, as well as conformations of the cyclohexane and pyrazole rings that stabilize structure by indirectly allowing for optimization of hydrogen bonding.

How might one embed a chemist's knowledge into an automated materials-discovery pipeline? In generative design for inorganic crystalline materials, generating candidate compounds is no longer a bottleneck – there are now synthetic datasets of millions of compounds. However, weeding out unsynthesizable or difficult to synthesize compounds remains an outstanding challenge. Post-generation “filters” have been proposed as a means of embedding human domain knowledge, either in the form of scientific laws or rules of thumb. Examples include charge neutrality, electronegativity balance, and energy above hull. Some filters are “hard” and some are “soft” — for example, it is difficult to envision creating a stable compound while violating the rule of charge neutrality; however, several compounds break the Hume-Rothery rules. It is therefore natural to wonder: can one compile a comprehensive list of “filters” that embed domain knowledge, adopt a principled approach to classifying them as either non-conditional or conditional “filters,” and envision a software environment to implement combinations of these in a systematic manner? In this commentary we explore such questions, “filters” for screening of novel inorganic compounds for synthesizability.

In this study, nuclear magnetic resonance (NMR) is used to investigate the crystallisation behaviour of aspirin within a mesoporous SBA-15 silica material. The potential of dynamic nuclear polarisation (DNP) experiments is also investigated using specifically designed porous materials that incorporate polarising agents within their walls. The formation of the metastable crystalline form II is observed when crystallisation occurs within the pores of the mesoporous structure. Conversely, bulk crystallisation yields the most stable form, namely form I, of aspirin. Remarkably, the metastable form II remains trapped within the pores of mesoporous SBA-15 silica material even 30 days after impregnation, underscoring its persistent stability within this confined environment.

Evolution of P450 BM3 is a topic of extensive research, but screening the various substrate/reaction combinations remains a time-consuming process. Indigo production has the potential to serve as a simple high-throughput method for reaction screening, as bacterial colonies expressing indigo (+) variants can be visually identified via their blue phenotype. Indigo (+) single variants, indigo (−) single variants and a combinatorial library, containing mutations that enable the blue phenotype, were screened for their ability to hydroxylate a panel of 12 aromatic compounds using the 4-aminoantipyrine colorimetric assay. Recombination of indigo (+) single variants to create a multiple-variant library is a particularly useful strategy, as all top performing P450 BM3 variants with high hydroxylation activity were either indigo (+) single variants or contained multiple substitutions. Furthermore, active variants, as determined using the 4-AAP assay, were further characterized and several variants were identified that gave more than 90% conversion with 1,3-dichlorobenzene and predominantly formed 2,6-dichlorophenol; other variants showed significant substrate selectivity. This supports the hypothesis that substitution at positions that enable the indigo (+) phenotype, or hotspot residues, is a general mechanism for increasing aromatic hydroxylation activity. Overall, this research demonstrates that indigo (+) single variants, identified via colorimetric colony-based screening, may be recombined to generate a multiply-substituted variant library containing many variants with high aromatic hydroxylation activity. The combination of colony-based screening and other screening assays greatly accelerates enzyme engineering, as readily-identified indigo (+) single variants can be recombined to create a library of active multiple variants without extensive screening of single variants.

We describe the problems of quantum chemistry, the intuition behind classical heuristic methods used to solve them, a conjectured form of the classical complexity of quantum chemistry problems, and the subsequent opportunities for quantum advantage. This article is written for both quantum chemists and quantum information theorists. In particular, we attempt to summarize the domain of quantum chemistry problems as well as the chemical intuition that is applied to solve them within concrete statements (such as a classical heuristic cost conjecture) in the hope that this may stimulate future analysis.

These concluding remarks summarize the Faraday Discussion on New Directions in Molecular Scattering. The discussion brought together scientists from a wide range of disciplines, from astrochemistry to coherent quantum control, and the submitted papers highlighted the need for innovation in experimental methods and computational tools to tackle more complex systems, relevant to chemistry in the real world. As recorded in the previous pages of this discussion, the meeting saw lively debate on numerous topical issues. This summary outlines some of the highlighted key developments in the field, and points towards future directions of molecular scattering research.