Phillip M. Maffettone, William Fletcher, Thomas Christian Nicholas, Volker L. Deringer, Jane R. Allison, Lorna Smith, Andrew Goodwin
The pair distribution function (PDF) is an important metric for characterising structure in complex materials, but it is well known that meaningfully different structural models can sometimes give rise to equivalent PDFs. In this paper, we discuss the use of model likelihoods as a general approach for discriminating between such homometric structure solutions. Drawing on two main case studies---one concerning the structure of a small peptide and the other amorphous calcium carbonate---we show how consideration of model likelihood can help drive robust structure solution even in cases where the PDF is particularly information poor. The obvious thread of these individual case studies is the potential role for machine learning approaches to help guide structure determination from the PDF, and our paper finishes with some forward-looking discussion along these lines.
对分布函数（PDF）是表征复杂材料结构的重要指标，但众所周知，有意义的不同结构模型有时会产生等效的 PDF。在本文中，我们将讨论如何使用模型似然值作为区分此类等效结构解的一般方法。通过两个主要的案例研究--一个是关于小肽的结构，另一个是关于无定形碳酸钙--我们展示了即使在 PDF 信息特别贫乏的情况下，考虑模型似然性如何有助于推动稳健的结构求解。这些单独案例研究的明显线索是机器学习方法在帮助指导 PDF 结构确定方面的潜在作用，我们的论文最后沿着这些线索进行了一些前瞻性讨论。
{"title":"When can we trust structural models derived from pair distribution function measurements?","authors":"Phillip M. Maffettone, William Fletcher, Thomas Christian Nicholas, Volker L. Deringer, Jane R. Allison, Lorna Smith, Andrew Goodwin","doi":"10.1039/d4fd00106k","DOIUrl":"https://doi.org/10.1039/d4fd00106k","url":null,"abstract":"The pair distribution function (PDF) is an important metric for characterising structure in complex materials, but it is well known that meaningfully different structural models can sometimes give rise to equivalent PDFs. In this paper, we discuss the use of model likelihoods as a general approach for discriminating between such homometric structure solutions. Drawing on two main case studies---one concerning the structure of a small peptide and the other amorphous calcium carbonate---we show how consideration of model likelihood can help drive robust structure solution even in cases where the PDF is particularly information poor. The obvious thread of these individual case studies is the potential role for machine learning approaches to help guide structure determination from the PDF, and our paper finishes with some forward-looking discussion along these lines.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"32 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christopher M Collins, Hasan Sayeed, George Darling, John B Claridge, Taylor D. Sparks, Matthew J. Rosseinsky
The prediction of new compounds via crystal structure prediction may transform how the materials chemistry community discovers new compounds. In the prediction of inorganic crystal structures there are three distinct classes of prediction; Performing crystal structure prediction via heuristic algorithms, using a range of established crystal structure prediction codes, an emerging community using generative machine learning models to predict crystal structures directly and the use of mathematical optimisation to solve crystal structures exactly. In this work, we demonstrate the combination of heuristic and generative machine learning, the use of a generative machine learning model to produce the starting population of crystal structures for a heuristic algorithm and discuss the benefits, demonstrating the method on eight known compounds with reported crystal structures and three hypothetical compounds. We show that the integration of machine learning structure generation with heuristic structure prediction results in both faster compute times per structure and lower energies. This work provides to the community a set of eleven compounds with varying chemistry and complexity that can be used as a benchmark for new crystal structure prediction methods as they emerge.
{"title":"Integration of generative machine learning with the heuristic crystal structure prediction code FUSE","authors":"Christopher M Collins, Hasan Sayeed, George Darling, John B Claridge, Taylor D. Sparks, Matthew J. Rosseinsky","doi":"10.1039/d4fd00094c","DOIUrl":"https://doi.org/10.1039/d4fd00094c","url":null,"abstract":"The prediction of new compounds via crystal structure prediction may transform how the materials chemistry community discovers new compounds. In the prediction of inorganic crystal structures there are three distinct classes of prediction; Performing crystal structure prediction via heuristic algorithms, using a range of established crystal structure prediction codes, an emerging community using generative machine learning models to predict crystal structures directly and the use of mathematical optimisation to solve crystal structures exactly. In this work, we demonstrate the combination of heuristic and generative machine learning, the use of a generative machine learning model to produce the starting population of crystal structures for a heuristic algorithm and discuss the benefits, demonstrating the method on eight known compounds with reported crystal structures and three hypothetical compounds. We show that the integration of machine learning structure generation with heuristic structure prediction results in both faster compute times per structure and lower energies. This work provides to the community a set of eleven compounds with varying chemistry and complexity that can be used as a benchmark for new crystal structure prediction methods as they emerge.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"68 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141190890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Scanning electrochemical cell microscopy (SECCM) is a powerful nanoscale electrochemical technique that advances our understanding of heterogeneity at the electrode-electrolyte interface. Dual-channel nanopipettes can often serve as the probe, and a voltage bias between the channels can control the local electrolyte environment via migration and electroosmotic flow (EOF). The ability to elucidate and predict the contribution of each transport is desirable. In this work, we measured the limiting current of different redox molecules to experimentally elucidate the contribution of migration and EOF at the droplet-substrate interface in SECCM. The results were further supported by fluorescence imaging and finite element modeling. We showed that redox mediators with high charge, such as Ru(NH3)63+, migration contributes 5× as much mass transport limiting current compared to EOF when a bias voltage is applied. The exact contribution of each mode at a given potential bias depends on the electrical double layer structure, which can be tuned by the surface charge and solution composition. The contribution can be quantitatively predicted in the finite element model. Our findings will enable the precise control of mass transport in dual-channel SECCM and potentially open new scanning modes in SECCM via precise control of reaction flux.
{"title":"Controlling Droplet Cell Environment in Scanning Electrochemical Cell Microscopy (SECCM) via Migration and Electroosmotic Flow","authors":"Samuel F Wenzel, Heekwon Lee, Hang Ren","doi":"10.1039/d4fd00080c","DOIUrl":"https://doi.org/10.1039/d4fd00080c","url":null,"abstract":"Scanning electrochemical cell microscopy (SECCM) is a powerful nanoscale electrochemical technique that advances our understanding of heterogeneity at the electrode-electrolyte interface. Dual-channel nanopipettes can often serve as the probe, and a voltage bias between the channels can control the local electrolyte environment via migration and electroosmotic flow (EOF). The ability to elucidate and predict the contribution of each transport is desirable. In this work, we measured the limiting current of different redox molecules to experimentally elucidate the contribution of migration and EOF at the droplet-substrate interface in SECCM. The results were further supported by fluorescence imaging and finite element modeling. We showed that redox mediators with high charge, such as Ru(NH3)63+, migration contributes 5× as much mass transport limiting current compared to EOF when a bias voltage is applied. The exact contribution of each mode at a given potential bias depends on the electrical double layer structure, which can be tuned by the surface charge and solution composition. The contribution can be quantitatively predicted in the finite element model. Our findings will enable the precise control of mass transport in dual-channel SECCM and potentially open new scanning modes in SECCM via precise control of reaction flux.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"39 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141168992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ion adsorption and dynamics in porous carbons is crucial for many technologies such as energy storage and desalination. Nuclear Magnetic Resonance (NMR) spectroscopy is a key method to investigate such systems thanks to the possibility to distinguish adsorbed (in-pore) and bulk (ex-pore) species in the spectra. However, the large variety of magnetic environments experienced by the ions adsorbed in the particles and the existence of dynamic exchange between the inside of the particles and the bulk renders the intepretation of the NMR experiments very complex. In this work, we optimise and apply a mesoscopic model to simulate NMR spectra of ions in systems where carbon particles of different sizes can be considered. We demonstrate that even for monodisperse systems, complex NMR spectra, with broad and narrow peaks, can be observed. We then show that the inclusion of polydispersity is essential to recover some experimentally observed features, such as the co-existence of peaks assigned to in-pore, exchange and bulk. Indeed, the variety of exchange rates between in-pore and ex-pore environments, present in experiments but not taken into account in analytical models, is necessary to reproduce the complexity of experimental NMR spectra.
{"title":"Investigating the effect of particle size distribution and complex exchange dynamics on NMR spectra of ions diffusing in disordered porous carbons through a mesoscopic model","authors":"El Hassane Lahrar, Celine Merlet","doi":"10.1039/d4fd00082j","DOIUrl":"https://doi.org/10.1039/d4fd00082j","url":null,"abstract":"Ion adsorption and dynamics in porous carbons is crucial for many technologies such as energy storage and desalination. Nuclear Magnetic Resonance (NMR) spectroscopy is a key method to investigate such systems thanks to the possibility to distinguish adsorbed (in-pore) and bulk (ex-pore) species in the spectra. However, the large variety of magnetic environments experienced by the ions adsorbed in the particles and the existence of dynamic exchange between the inside of the particles and the bulk renders the intepretation of the NMR experiments very complex. In this work, we optimise and apply a mesoscopic model to simulate NMR spectra of ions in systems where carbon particles of different sizes can be considered. We demonstrate that even for monodisperse systems, complex NMR spectra, with broad and narrow peaks, can be observed. We then show that the inclusion of polydispersity is essential to recover some experimentally observed features, such as the co-existence of peaks assigned to in-pore, exchange and bulk. Indeed, the variety of exchange rates between in-pore and ex-pore environments, present in experiments but not taken into account in analytical models, is necessary to reproduce the complexity of experimental NMR spectra.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"69 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Susana Ramalhete, Hayley Green, Jesús Angulo, Dinu Iuga, László Fábián, Gareth O. Lloyd, Yaroslav Z. Khimyak
Supramolecular hydrogels have a wide range of applications in the biomedical field, acting as scaffolds for cell culture, matrices for tissue engineering and vehicles for drug delivery. L-Phenylalanine (Phe) is a natural amino acid that plays a significant role in physiological and pathophysiological processes (phenylketonuria and assembly of fibrils linked to tissue damage). Since Myerson et al. (2002) reported that Phe forms a fibrous network in vitro, Phe’s self-assembly processes in water have been thoroughly investigated. We have reported structural control over gelation by introduction of a halogen atom in the aromatic ring of Phe, driving changes in the packing motifs, and therefore, dictating gelation functionality. The additional level of control gained by the preparation of multi-component gel systems offers significant advantages in tuning functional properties of such materials. Gaining molecular level information on the distribution of gelators between the inherent structural and dynamic heterogeneities of these materials remains a considerable challenge. Using multicomponent gels based on Phe and amino-L-phenylalanine (NH2-Phe) we explored the patterns of ordered/disordered domains in the gel fibres and will attempt to come up with general trends of interactions in the gel fibres and at the fibre/solution interfaces. Phe and NH2-Phe were found to self-assemble in water into crystalline hydrogels. The determined faster dynamics of exchange between gel and solution states of NH2-Phe in comparison with Phe was correlated with weaker intermolecular interactions, highlighting the role of head groups in dictating the strength of intermolecular interactions. In the mixed Phe/ NH2-Phe systems, at low concentration of NH2-Phe, disruption of the network was promoted by interference of the aliphatics of NH2-Phe with electrostatic interactions between Phe molecules. At high concentrations of NH2-Phe, multiple gelator hydrogels were formed with crystal habits different from those of the pure gel fibres. NMR crystallography approaches combining the strengths of solid- and solution state NMR proved particularly suitable to obtain structural and dynamic insights into “ordered” fibres, solution phase and fibre/solution interfaces in these gels. These findings are supported by the plethora of experimental (diffraction, rheology, microscopy, thermal analysis) and computational (crystal structure prediction, DFT based approaches and MD simulations) methods.
{"title":"Probing assembly/disassembly of ordered molecular hydrogels","authors":"Susana Ramalhete, Hayley Green, Jesús Angulo, Dinu Iuga, László Fábián, Gareth O. Lloyd, Yaroslav Z. Khimyak","doi":"10.1039/d4fd00081a","DOIUrl":"https://doi.org/10.1039/d4fd00081a","url":null,"abstract":"Supramolecular hydrogels have a wide range of applications in the biomedical field, acting as scaffolds for cell culture, matrices for tissue engineering and vehicles for drug delivery. L-Phenylalanine (Phe) is a natural amino acid that plays a significant role in physiological and pathophysiological processes (phenylketonuria and assembly of fibrils linked to tissue damage). Since Myerson et al. (2002) reported that Phe forms a fibrous network in vitro, Phe’s self-assembly processes in water have been thoroughly investigated. We have reported structural control over gelation by introduction of a halogen atom in the aromatic ring of Phe, driving changes in the packing motifs, and therefore, dictating gelation functionality. The additional level of control gained by the preparation of multi-component gel systems offers significant advantages in tuning functional properties of such materials. Gaining molecular level information on the distribution of gelators between the inherent structural and dynamic heterogeneities of these materials remains a considerable challenge. Using multicomponent gels based on Phe and amino-L-phenylalanine (NH2-Phe) we explored the patterns of ordered/disordered domains in the gel fibres and will attempt to come up with general trends of interactions in the gel fibres and at the fibre/solution interfaces. Phe and NH2-Phe were found to self-assemble in water into crystalline hydrogels. The determined faster dynamics of exchange between gel and solution states of NH2-Phe in comparison with Phe was correlated with weaker intermolecular interactions, highlighting the role of head groups in dictating the strength of intermolecular interactions. In the mixed Phe/ NH2-Phe systems, at low concentration of NH2-Phe, disruption of the network was promoted by interference of the aliphatics of NH2-Phe with electrostatic interactions between Phe molecules. At high concentrations of NH2-Phe, multiple gelator hydrogels were formed with crystal habits different from those of the pure gel fibres. NMR crystallography approaches combining the strengths of solid- and solution state NMR proved particularly suitable to obtain structural and dynamic insights into “ordered” fibres, solution phase and fibre/solution interfaces in these gels. These findings are supported by the plethora of experimental (diffraction, rheology, microscopy, thermal analysis) and computational (crystal structure prediction, DFT based approaches and MD simulations) methods.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"52 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141146591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zainab Rehman, Jairah Lubay, W. Trent Trent Franks, Emily Corlett, Bao Nguyen, Garry Scrivens, Brian Samas, Heather Frericks-Schmidt, Steven P. Brown
The application of NMR crystallography to organic molecules is exemplified by two case studies. For the tosylate salt of the active pharmaceutical ingredient, Ritlectinib, solid-state NMR spectra are presented at a 1H Larmor frequency of 1 GHz and a magic-angle spinning (MAS) frequency of 60 kHz. Specifically, 14N-1H heteronuclear multiple-quantum coherence (HMQC) and 1H-1H double-quantum (DQ) single-quantum (SQ) correlation experiments are powerful probes of hydrogen bonding interactions. A full assignment of the 1H, 13C and 14N/ 15N chemical shifts is achieved using also 1H-13C cross polarization (CP) HETCOR spectra together with gauge-including projector augmented wave (GIPAW) DFT calculation for the geometry-optimised X-ray diffraction crystal structure that is reported here (CCDC 2352028). In addition, GIPAW calculations are presented for the 13C chemical shifts in the two polymorphs of cellulose for which diffraction structures are available. For both case studies, a focus is on the discrepancy between experiment and GIPAW calculation.
{"title":"Organic NMR Crystallography: Enabling Progress for Applications to Pharmaceuticals and Plant Cell Walls","authors":"Zainab Rehman, Jairah Lubay, W. Trent Trent Franks, Emily Corlett, Bao Nguyen, Garry Scrivens, Brian Samas, Heather Frericks-Schmidt, Steven P. Brown","doi":"10.1039/d4fd00088a","DOIUrl":"https://doi.org/10.1039/d4fd00088a","url":null,"abstract":"The application of NMR crystallography to organic molecules is exemplified by two case studies. For the tosylate salt of the active pharmaceutical ingredient, Ritlectinib, solid-state NMR spectra are presented at a 1H Larmor frequency of 1 GHz and a magic-angle spinning (MAS) frequency of 60 kHz. Specifically, 14N-1H heteronuclear multiple-quantum coherence (HMQC) and 1H-1H double-quantum (DQ) single-quantum (SQ) correlation experiments are powerful probes of hydrogen bonding interactions. A full assignment of the 1H, 13C and 14N/ 15N chemical shifts is achieved using also 1H-13C cross polarization (CP) HETCOR spectra together with gauge-including projector augmented wave (GIPAW) DFT calculation for the geometry-optimised X-ray diffraction crystal structure that is reported here (CCDC 2352028). In addition, GIPAW calculations are presented for the 13C chemical shifts in the two polymorphs of cellulose for which diffraction structures are available. For both case studies, a focus is on the discrepancy between experiment and GIPAW calculation.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"47 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141146610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sebastian A Ramos, Leonard J Mueller, Gregory J. O. Beran
Ab initio chemical shift prediction plays a central role in nuclear magnetic resonance (NMR) crystallography, and the accuracy with which chemical shifts can be predicted relative to experiment impacts the confidence with which structures can be assigned. For organic crystals, periodic density functional theory calculations with the gauge-including projector augmented wave (GIPAW) approximation and the PBE functional are widely used at present. Many previous studies have examined how using more advanced density functionals can increase the accuracy of predicted chemical shifts relative to experiment, but nearly all of those studies employed crystal structures that were optimized with generalized-gradient approximation (GGA) functionals. Here, we investigate how the accuracy of the predicted chemical shifts in organic crystals is affected by replacing GGA-level PBE-D3(BJ) crystal geometries with more accurate hybrid functional PBE0-D3(BJ) ones. Based on benchmark data sets containing 132 13C and 35 15N chemical shifts plus case studies on testosterone, acetaminophen, and phenobarbital, we find that switching from GGA-level geometries and chemical shifts to hybrid functional ones reduces 13C and 15N chemical shift errors by ~40-60% versus experiment. However, most of the improvement stems from the use of the hybrid functional for the chemical shift calculations, rather than from the refined geometries. In addition, even with the improved geometries, we find that double-hybrid functionals still do not systematically increase chemical shift agreement with experiment beyond what hybrid functionals provide. In the end, these results suggest that the combination of GGA-level crystal structures and hybrid-functional chemical shifts represents a particularly cost-effective combination for NMR crystallography in organic systems.
{"title":"The interplay of density functional selection and crystal structure for accurate NMR chemical shift predictions","authors":"Sebastian A Ramos, Leonard J Mueller, Gregory J. O. Beran","doi":"10.1039/d4fd00072b","DOIUrl":"https://doi.org/10.1039/d4fd00072b","url":null,"abstract":"Ab initio chemical shift prediction plays a central role in nuclear magnetic resonance (NMR) crystallography, and the accuracy with which chemical shifts can be predicted relative to experiment impacts the confidence with which structures can be assigned. For organic crystals, periodic density functional theory calculations with the gauge-including projector augmented wave (GIPAW) approximation and the PBE functional are widely used at present. Many previous studies have examined how using more advanced density functionals can increase the accuracy of predicted chemical shifts relative to experiment, but nearly all of those studies employed crystal structures that were optimized with generalized-gradient approximation (GGA) functionals. Here, we investigate how the accuracy of the predicted chemical shifts in organic crystals is affected by replacing GGA-level PBE-D3(BJ) crystal geometries with more accurate hybrid functional PBE0-D3(BJ) ones. Based on benchmark data sets containing 132 <small><sup>13</sup></small>C and 35 <small><sup>15</sup></small>N chemical shifts plus case studies on testosterone, acetaminophen, and phenobarbital, we find that switching from GGA-level geometries and chemical shifts to hybrid functional ones reduces <small><sup>13</sup></small>C and <small><sup>15</sup></small>N chemical shift errors by ~40-60% versus experiment. However, most of the improvement stems from the use of the hybrid functional for the chemical shift calculations, rather than from the refined geometries. In addition, even with the improved geometries, we find that double-hybrid functionals still do not systematically increase chemical shift agreement with experiment beyond what hybrid functionals provide. In the end, these results suggest that the combination of GGA-level crystal structures and hybrid-functional chemical shifts represents a particularly cost-effective combination for NMR crystallography in organic systems.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"19 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141061921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-16DOI: 10.1038/s41572-024-00519-9
K. Kahle, Petra M. Klinge, Jenna E. Koschnitzky, Abhaya V. Kulkarni, Nanna MacAulay, Shenandoah Robinson, Steven J. Schiff, J. Strahle
{"title":"Paediatric hydrocephalus","authors":"K. Kahle, Petra M. Klinge, Jenna E. Koschnitzky, Abhaya V. Kulkarni, Nanna MacAulay, Shenandoah Robinson, Steven J. Schiff, J. Strahle","doi":"10.1038/s41572-024-00519-9","DOIUrl":"https://doi.org/10.1038/s41572-024-00519-9","url":null,"abstract":"","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"23 9","pages":"1-16"},"PeriodicalIF":81.5,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140967670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Javier Valenzuela Reina, Federico Civaia, Angela F Harper, Christoph Scheurer, Simone Köcher
We present a comprehensive study on the best practices for integrating first principles simulations in experimental quadrupolar solid-state nuclear magnetic resonance (SS-NMR), exploiting the synergies between theory and experiment for achieving the optimal interpretation of both. Most high performance materials (HPM), such as battery electrodes, exhibit complex SS-NMR spectra due to dynamic effects or amorphous phases. NMR crystallography for such challenging materials requires reliable, accurate, efficient computational methods for calculating NMR observables from first principles for the transfer between material structure models and the interpretation of their experimental SS-NMR spectra. NMR-active nuclei within HPMs are routinely probed by their chemical shielding anisotropy (CSA). However, several nuclear isotopes of interest, e.g. 7Li and 27Al, have a nuclear quadrupole and experience additional interactions with the surrounding electric field gradient (EFG). The quadrupolar interaction is a valuable source of information about atomistic structure and in particular local symmetry, complementing the CSA. As such, there is a range of different methods and codes to choose from for calculating EFGs, from all-electron to plane wave methods. We benchmark the accuracy of different simulation strategies for computing the EFG tensor of quadrupolar nuclei with plane wave density functional theory (DFT) and study the impact of the material structure, and the details of the simulation strategy. Especially for small nuclei with few electrons, such as 7Li, we can show that the choice of physical approximations and simulation parameters has a large effect on the transferability of the simulations results. To the best of our knowledge, we present the first comprehensive reference scale and literature survey for 7Li quadrupolar couplings. The results allow us to establish practical guidelines for developing the best simulation strategy for correlating DFT to experimental data extracting the maximum benefit and information from both, thereby advancing further research into HPMs.
{"title":"The EFG Rosetta Stone: Translating between DFT calculations and solid state NMR experiments","authors":"Javier Valenzuela Reina, Federico Civaia, Angela F Harper, Christoph Scheurer, Simone Köcher","doi":"10.1039/d4fd00075g","DOIUrl":"https://doi.org/10.1039/d4fd00075g","url":null,"abstract":"We present a comprehensive study on the best practices for integrating first principles simulations in experimental quadrupolar solid-state nuclear magnetic resonance (SS-NMR), exploiting the synergies between theory and experiment for achieving the optimal interpretation of both. Most high performance materials (HPM), such as battery electrodes, exhibit complex SS-NMR spectra due to dynamic effects or amorphous phases. NMR crystallography for such challenging materials requires reliable, accurate, efficient computational methods for calculating NMR observables from first principles for the transfer between material structure models and the interpretation of their experimental SS-NMR spectra. NMR-active nuclei within HPMs are routinely probed by their chemical shielding anisotropy (CSA). However, several nuclear isotopes of interest, e.g. <small><sup>7</sup></small>Li and <small><sup>27</sup></small>Al, have a nuclear quadrupole and experience additional interactions with the surrounding electric field gradient (EFG). The quadrupolar interaction is a valuable source of information about atomistic structure and in particular local symmetry, complementing the CSA. As such, there is a range of different methods and codes to choose from for calculating EFGs, from all-electron to plane wave methods. We benchmark the accuracy of different simulation strategies for computing the EFG tensor of quadrupolar nuclei with plane wave density functional theory (DFT) and study the impact of the material structure, and the details of the simulation strategy. Especially for small nuclei with few electrons, such as <small><sup>7</sup></small>Li, we can show that the choice of physical approximations and simulation parameters has a large effect on the transferability of the simulations results. To the best of our knowledge, we present the first comprehensive reference scale and literature survey for <small><sup>7</sup></small>Li quadrupolar couplings. The results allow us to establish practical guidelines for developing the best simulation strategy for correlating DFT to experimental data extracting the maximum benefit and information from both, thereby advancing further research into HPMs.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"2015 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140935460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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