The kinetics of particle nucleation and growth are critical to a wide variety of electrochemical systems. While studies carried out at the single particle level are promising for improving our understanding of nucleation and growth processes, conventional analytical frameworks commonly employed in bulk studies may not be appropriate for single particle experiments. Here, we present scanning electrochemical cell microsocpy (SECCM) studies of Ag nucleation and growth on carbon and indium tin oxide (ITO) electrodes. Statistical analyses of the data from these experiments reveal significant discrepancies with traditional, quasi-equilibrium kinetic models commonly employed in the analysis of particle nucleation in electrochemical systems. Time-dependent kinetic models are presented capable of appropriately analysing the data generated via SECCM to extract meaningful chemical quantities such as surface energies and kinetic rate constants. These results demonstrate a powerful new approach to the analysis of single particle nucleation and growth data which could be leveraged in differentiating behavior within spatially heterogeneous systems.
{"title":"Electrochemical Nucleation and Growth Kinetics: Insights from Single Particle Scanning Electrochemical Cell Microscopy Studies","authors":"Kenneth Osoro, Caleb Hill","doi":"10.1039/d4fd00131a","DOIUrl":"https://doi.org/10.1039/d4fd00131a","url":null,"abstract":"The kinetics of particle nucleation and growth are critical to a wide variety of electrochemical systems. While studies carried out at the single particle level are promising for improving our understanding of nucleation and growth processes, conventional analytical frameworks commonly employed in bulk studies may not be appropriate for single particle experiments. Here, we present scanning electrochemical cell microsocpy (SECCM) studies of Ag nucleation and growth on carbon and indium tin oxide (ITO) electrodes. Statistical analyses of the data from these experiments reveal significant discrepancies with traditional, quasi-equilibrium kinetic models commonly employed in the analysis of particle nucleation in electrochemical systems. Time-dependent kinetic models are presented capable of appropriately analysing the data generated via SECCM to extract meaningful chemical quantities such as surface energies and kinetic rate constants. These results demonstrate a powerful new approach to the analysis of single particle nucleation and growth data which could be leveraged in differentiating behavior within spatially heterogeneous systems.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"30 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141573175","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}
We describe a single-molecule electrochemical imaging strategy to study the electrocatalytic (EC') mechanism. Using the fluorescent molecule ATTO647N at extremely low concentrations as the substrate, we confirmed its catalytic reduction to a nonfluorescence form in the presence of the mediator phenazine methosulfate (PMS) by imaging and counting fluorescent molecules. Conventional electrochemical current in cyclic voltammetry would not have allowed us to infer the existence of an EC’ process or the PMS-mediated ATTO647N reduction. Additionally, we observed shifts in the catalytic reduction potential of ATTO647N at various mediator concentrations, which agree with the theoretical predictions by Savéant. Our work offers a new perspective on connecting single-molecule EC’ behaviors with the conventional ensemble EC’ mechanism, both practically and theoretically.
{"title":"Single-molecule electrochemical imaging of 'split waves' in the electrocatalytic (EC') mechanism","authors":"Wandong Zhao, Jin Lu","doi":"10.1039/d4fd00126e","DOIUrl":"https://doi.org/10.1039/d4fd00126e","url":null,"abstract":"We describe a single-molecule electrochemical imaging strategy to study the electrocatalytic (EC') mechanism. Using the fluorescent molecule ATTO647N at extremely low concentrations as the substrate, we confirmed its catalytic reduction to a nonfluorescence form in the presence of the mediator phenazine methosulfate (PMS) by imaging and counting fluorescent molecules. Conventional electrochemical current in cyclic voltammetry would not have allowed us to infer the existence of an EC’ process or the PMS-mediated ATTO647N reduction. Additionally, we observed shifts in the catalytic reduction potential of ATTO647N at various mediator concentrations, which agree with the theoretical predictions by Savéant. Our work offers a new perspective on connecting single-molecule EC’ behaviors with the conventional ensemble EC’ mechanism, both practically and theoretically.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"11 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141573176","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}
Single-entity electrochemistry (SEE) is an emerging field within electrochemistry focused on investigating individual entities such as nanoparticles, bacteria, cells, or single molecules. Accurate identification and analysis of SEE signals require effective data processing methods for unbiased and automated feature extraction. In this study, we apply and compare two approaches for step detection in SEE data: discrete wavelet transforms (DWT) and convolutional neural networks (CNN).
单实体电化学（SEE）是电化学中的一个新兴领域，重点研究纳米粒子、细菌、细胞或单分子等单个实体。要准确识别和分析 SEE 信号，需要有效的数据处理方法，以实现无偏的自动特征提取。在本研究中，我们应用并比较了 SEE 数据中阶跃检测的两种方法：离散小波变换 (DWT) 和卷积神经网络 (CNN)。
{"title":"Advanced Algorithm for Step Detection in Single-Entity Electrochemistry: A Comparative Study of Wavelet Transforms and Convolutional Neural Networks","authors":"Ziwen Zhao, Arunava Naha, Nikolaos Kostopoulos, Alina Sekretareva","doi":"10.1039/d4fd00130c","DOIUrl":"https://doi.org/10.1039/d4fd00130c","url":null,"abstract":"Single-entity electrochemistry (SEE) is an emerging field within electrochemistry focused on investigating individual entities such as nanoparticles, bacteria, cells, or single molecules. Accurate identification and analysis of SEE signals require effective data processing methods for unbiased and automated feature extraction. In this study, we apply and compare two approaches for step detection in SEE data: discrete wavelet transforms (DWT) and convolutional neural networks (CNN).","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"87 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552191","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}
Local electrochemical impedance spectroscopy (LEIS) has emerged to characterize local electrochemical processes on heterogeneous surfaces. However, the current LEIS heavily relies on lock-in amplifier that has a poor gain effect for weak current, limiting the achievement of high-spatial imaging. Herein, an integrated scanning electrochemical cell microscopy is developed by directly collecting the alternating current (AC) current signal through a preamplifier. The recorded local current (sub nA-level) is compared with the initial excitation signal to get the parameters for Nyquist plotting. By integrating this method into a scanning electrochemical cell microscopy (SECCM), an image of LEIS at the Indium Tin Oxide/gold (ITO/Au) electrode is obtained with a spatial resolution of 180 nm. The established SECCM platform is integrated that could be positioned into the limited space (e.g. glove box) for real characterization of electrodes.
局部电化学阻抗光谱法（LEIS）是为描述异质表面的局部电化学过程而出现的。然而，目前的局部电化学阻抗光谱主要依赖于锁相放大器，该放大器对微弱电流的增益效果不佳，限制了高空间成像的实现。在此，我们开发了一种集成扫描电化学细胞显微镜，通过前置放大器直接采集交流电流信号。记录的局部电流（亚 nA 级）与初始激励信号进行比较，以获得奈奎斯特绘图参数。通过将此方法集成到扫描电化学电池显微镜（SECCM）中，可获得铟锡氧化物/金（ITO/Au）电极的 LEIS 图像，空间分辨率为 180 nm。已建立的 SECCM 平台可集成到有限的空间（如手套箱）中，用于电极的实际表征。
{"title":"Integrated Scanning Electrochemical Cell Microscopy Platform with Local Electrochemical Impedance Spectroscopy using Preamplifier","authors":"Ancheng Wang, Rong Jin, Dechen Jiang","doi":"10.1039/d4fd00122b","DOIUrl":"https://doi.org/10.1039/d4fd00122b","url":null,"abstract":"Local electrochemical impedance spectroscopy (LEIS) has emerged to characterize local electrochemical processes on heterogeneous surfaces. However, the current LEIS heavily relies on lock-in amplifier that has a poor gain effect for weak current, limiting the achievement of high-spatial imaging. Herein, an integrated scanning electrochemical cell microscopy is developed by directly collecting the alternating current (AC) current signal through a preamplifier. The recorded local current (sub nA-level) is compared with the initial excitation signal to get the parameters for Nyquist plotting. By integrating this method into a scanning electrochemical cell microscopy (SECCM), an image of LEIS at the Indium Tin Oxide/gold (ITO/Au) electrode is obtained with a spatial resolution of 180 nm. The established SECCM platform is integrated that could be positioned into the limited space (e.g. glove box) for real characterization of electrodes.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"45 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501891","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}
Herein is presented our discovery of an electrochemical approach for modulating fluorophores between fluorescent bright states and dim states. We demonstrate how to effectively modulate the fluorescent intensity of organic dye-labelled cell samples on an indium tin oxide surface using electrochemistry with redox-active mediators present in an oxygen scavenger buffer. We showed the electrochemical fluorescence modulation is sensitive to the applied potential and the excitation laser intensity, indicating the possibility of coupled photochemical and electrochemical reactions occurring. We also compared the electrochemical fluorescence modulation of representative oxazine, rhodamine, and cyanine dyes using ATTO 655, Alexa Fluor 488, and Alexa Fluor 647. Different dyes with distinctly different structural core show fluorescence modulation to different extents. The electrochemical fluorescence modulation will be applicable in fluorescence imaging techniques as well as biosensing.
{"title":"The electrochemical modulation of single molecule fluorescence","authors":"Ying Yang, Yuanqing Ma, John Justin Gooding","doi":"10.1039/d4fd00111g","DOIUrl":"https://doi.org/10.1039/d4fd00111g","url":null,"abstract":"Herein is presented our discovery of an electrochemical approach for modulating fluorophores between fluorescent bright states and dim states. We demonstrate how to effectively modulate the fluorescent intensity of organic dye-labelled cell samples on an indium tin oxide surface using electrochemistry with redox-active mediators present in an oxygen scavenger buffer. We showed the electrochemical fluorescence modulation is sensitive to the applied potential and the excitation laser intensity, indicating the possibility of coupled photochemical and electrochemical reactions occurring. We also compared the electrochemical fluorescence modulation of representative oxazine, rhodamine, and cyanine dyes using ATTO 655, Alexa Fluor 488, and Alexa Fluor 647. Different dyes with distinctly different structural core show fluorescence modulation to different extents. The electrochemical fluorescence modulation will be applicable in fluorescence imaging techniques as well as biosensing.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"8 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141527889","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}
The only NMR active oxygen isotope, Oxygen-17($^{17}text{O}$ ), serves as a sensitive probe due to its large chemical shift range, the electric field gradient at the oxygen site, and the quadrupolar interaction. Consequently, $^{17}text{O}$ solid-state NMR offers unique insights into local structures and finds significant applications in the study of disorder, reactivity, and host-guest chemistry. Despite recent advances in sensitivity enhancement, isotopic labeling, and NMR crystallography, the application of $^{17}text{O}$ solid-state NMR is still hindered by low natural abundance, costly enrichment, and challenges in handling spectrum signals. Density functional theory calculations and machine learning techniques offer an alternative approach to mapping the local crystal structures to NMR parameters. However, the lack of high-quality data remains a challenge, despite the establishment of some datasets. In this study, we implement and execute a high-throughput workflow combining AiiDA and Castep to evaluate the NMR parameters. Focusing on non-magnetic oxides, we have collected over 7100 binary, ternary, and quaternary compounds from the Materials Project and performed calculations. Furthermore, using various descriptors for the local crystalline environments, we model the $^{17}text{O}$ NMR using machine learning techniques, further enhancing our ability to predict and understand $^{17}text{O}$ NMR parameters in oxide crystals.
{"title":"High Throughput calculations and machine learning modeling of $^{17}text{O}$ NMR in non-magnetic oxides","authors":"Zhiyuan Li, Bo Zhao, Hongbin Zhang, Yixuan Zhang","doi":"10.1039/d4fd00128a","DOIUrl":"https://doi.org/10.1039/d4fd00128a","url":null,"abstract":"The only NMR active oxygen isotope, Oxygen-17($^{17}text{O}$ ), serves as a sensitive probe due to its large chemical shift range, the electric field gradient at the oxygen site, and the quadrupolar interaction. Consequently, $^{17}text{O}$ solid-state NMR offers unique insights into local structures and finds significant applications in the study of disorder, reactivity, and host-guest chemistry. Despite recent advances in sensitivity enhancement, isotopic labeling, and NMR crystallography, the application of $^{17}text{O}$ solid-state NMR is still hindered by low natural abundance, costly enrichment, and challenges in handling spectrum signals. Density functional theory calculations and machine learning techniques offer an alternative approach to mapping the local crystal structures to NMR parameters. However, the lack of high-quality data remains a challenge, despite the establishment of some datasets. In this study, we implement and execute a high-throughput workflow combining AiiDA and Castep to evaluate the NMR parameters. Focusing on non-magnetic oxides, we have collected over 7100 binary, ternary, and quaternary compounds from the Materials Project and performed calculations. Furthermore, using various descriptors for the local crystalline environments, we model the $^{17}text{O}$ NMR using machine learning techniques, further enhancing our ability to predict and understand $^{17}text{O}$ NMR parameters in oxide crystals.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"12 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141527890","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}
We have developed a new scanning probe approach for the delivery of a gas-phase reactant to the surface of an electrocatalyst through a self-replenishing bubble located at the end of a scanning probe. This approach enables local electrocatalytic rates to be detected under very-high mass transport rates due to the small distance between the gas-phase reactant in the bubble and the electrocatalyst surface. Here we report experiments for the delivery of carbon dioxide to a gold ultramicroelectrode surface using a micron-scale nanopipette. The approach curve profiles that we measure suggest a complex interplay between carbon dioxide reduction and hydrogen evolution which is mediated by both the probe-electrode distance and the potential of the gold ultramicroelectrode.
{"title":"Delivery of Carbon Dioxide to an Electrode Surface Using a Nanopipette","authors":"Jaimy Monteiro, Harry Dunne, Kim McKelvey","doi":"10.1039/d4fd00124a","DOIUrl":"https://doi.org/10.1039/d4fd00124a","url":null,"abstract":"We have developed a new scanning probe approach for the delivery of a gas-phase reactant to the surface of an electrocatalyst through a self-replenishing bubble located at the end of a scanning probe. This approach enables local electrocatalytic rates to be detected under very-high mass transport rates due to the small distance between the gas-phase reactant in the bubble and the electrocatalyst surface. Here we report experiments for the delivery of carbon dioxide to a gold ultramicroelectrode surface using a micron-scale nanopipette. The approach curve profiles that we measure suggest a complex interplay between carbon dioxide reduction and hydrogen evolution which is mediated by both the probe-electrode distance and the potential of the gold ultramicroelectrode.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"21 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141527891","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}
Colan E Hughes, Naomi V. Ratnasingam, Andrew Williams, Erwan Benhenou, Rhian Patterson, Kenneth D M Harris
We present a discussion of the range of NMR techniques that have been utilized for in-situ monitoring of crystallization processes, highlighting the opportunities that now exist for exploiting the versatility of NMR techniques to reveal insights into the changes that occur in both the solid phase and the liquid phase as a function of time during crystallization processes from solution. New results are presented on: (i) crystallization of glycine from aqueous solution at low temperature, revealing the relatively long-lived existence of a pure phase of the highly meta-stable β polymorph, (ii) the complementarity of 1H→13C cross-polarization NMR and direct-excitation 13C NMR techniques in probing the evolution of the solid and liquid phases in in-situ studies of crystallization processes, (iii) in-situ NMR studies of the process of guest exchange between a crystalline host-guest material in contact with the liquid phase of a more favourable guest molecule, and (iv) systematic studies of the influence of magic-angle sample spinning on the behaviour of a crystallization system.
{"title":"NMR Crystallization: In-Situ NMR Strategies for Monitoring the Evolution of Crystallization Processes","authors":"Colan E Hughes, Naomi V. Ratnasingam, Andrew Williams, Erwan Benhenou, Rhian Patterson, Kenneth D M Harris","doi":"10.1039/d4fd00079j","DOIUrl":"https://doi.org/10.1039/d4fd00079j","url":null,"abstract":"We present a discussion of the range of NMR techniques that have been utilized for in-situ monitoring of crystallization processes, highlighting the opportunities that now exist for exploiting the versatility of NMR techniques to reveal insights into the changes that occur in both the solid phase and the liquid phase as a function of time during crystallization processes from solution. New results are presented on: (i) crystallization of glycine from aqueous solution at low temperature, revealing the relatively long-lived existence of a pure phase of the highly meta-stable β polymorph, (ii) the complementarity of 1H→13C cross-polarization NMR and direct-excitation 13C NMR techniques in probing the evolution of the solid and liquid phases in in-situ studies of crystallization processes, (iii) in-situ NMR studies of the process of guest exchange between a crystalline host-guest material in contact with the liquid phase of a more favourable guest molecule, and (iv) systematic studies of the influence of magic-angle sample spinning on the behaviour of a crystallization system.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"45 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501892","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}
Ilia Kevlishvili, Roland Gerard St. Michel, Aaron Garrison, Jacob Toney, Husain Adamji, Hao-Jun Jia, Yuriy Roman-Leshkov, Heather Kulik
The breadth of transition metal chemical space covered by databases such as the Cambridge Structural Database and the derived computational database tmQM is not conducive to application-specific modeling and the development of structure–property relationships. Here, we employ both supervised and unsupervised natural language processing (NLP) techniques to link experimentally synthesized compounds in the tmQM database to their respective applications. Leveraging NLP models, we curate four distinct datasets: tmCAT for catalysis, tmPHOTO for photophysical activity, tmBIO for biological relevance, and tmSCO for magnetism. Analyzing the chemical substructures within each dataset reveals common chemical motifs in each of the designated applications. We then use these common chemical structures to augment our initial datasets for each application, yielding a total of 21,631 compounds in tmCAT, 4,599 in tmPHOTO, 2,782 in tmBIO, and 983 in tmSCO. These datasets are expected to accelerate the more targeted computational screening and development of refined structure–property relationships with machine learning.
{"title":"Leveraging natural language processing to curate the tmCAT, tmPHOTO, tmBIO, and tmSCO datasets of functional transition metal complexes","authors":"Ilia Kevlishvili, Roland Gerard St. Michel, Aaron Garrison, Jacob Toney, Husain Adamji, Hao-Jun Jia, Yuriy Roman-Leshkov, Heather Kulik","doi":"10.1039/d4fd00087k","DOIUrl":"https://doi.org/10.1039/d4fd00087k","url":null,"abstract":"The breadth of transition metal chemical space covered by databases such as the Cambridge Structural Database and the derived computational database tmQM is not conducive to application-specific modeling and the development of structure–property relationships. Here, we employ both supervised and unsupervised natural language processing (NLP) techniques to link experimentally synthesized compounds in the tmQM database to their respective applications. Leveraging NLP models, we curate four distinct datasets: tmCAT for catalysis, tmPHOTO for photophysical activity, tmBIO for biological relevance, and tmSCO for magnetism. Analyzing the chemical substructures within each dataset reveals common chemical motifs in each of the designated applications. We then use these common chemical structures to augment our initial datasets for each application, yielding a total of 21,631 compounds in tmCAT, 4,599 in tmPHOTO, 2,782 in tmBIO, and 983 in tmSCO. These datasets are expected to accelerate the more targeted computational screening and development of refined structure–property relationships with machine learning.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"34 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141527984","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}
Solid-State NMR has established itself as a cutting-egde spectroscopy for elucidating the structure of oxide glasses thanks to several decades of methodological and instrumental progresses. First-principles calculations of NMR properties combined with molecular dynamics (MD) simulations provides a powerful complementing approach for the interpretation of the NMR data although they still suffer from limitations in terms of size, time and high consumption of computational resources. We address this challenge by developing a machine-learning framework to boost predictive modelling of NMR spectra. We use kernel ridge regression techniques (least-square support vector regression and linear ridge regression) combined with the smooth overlap of atomic position (SOAP) atom-centered descriptors to efficiently predict NMR interactions: isotropic magnetic shielding and the electric field gradient (EFG) tensor. As illustrated in this work, this approach enables the simulation of MAS and MQMAS NMR spectra of very large models (more than 10000 atoms) and an efficient averaging of NMR properties over MD trajectories of nanoseconds for incorporating finite temperature effects, at computational cost of classical MD simulation. We illustrate these advances on sodium silicate glasses (SiO2-Na2O). NMR parameters (isotropic chemical shift and electric field gradient) could be predicted with an accuracy of 1 to 2% in terms of the total span of the NMR parameter values. To include vibrational effects, an approach is proposed by scaling the EFG tensor in NMR simulations with a factor obtained from the time auto-correlation functions computed on MD trajectory.
{"title":"First-principles NMR of oxide glasses boosted by machine learning","authors":"Thibault Charpentier","doi":"10.1039/d4fd00129j","DOIUrl":"https://doi.org/10.1039/d4fd00129j","url":null,"abstract":"Solid-State NMR has established itself as a cutting-egde spectroscopy for elucidating the structure of oxide glasses thanks to several decades of methodological and instrumental progresses. First-principles calculations of NMR properties combined with molecular dynamics (MD) simulations provides a powerful complementing approach for the interpretation of the NMR data although they still suffer from limitations in terms of size, time and high consumption of computational resources. We address this challenge by developing a machine-learning framework to boost predictive modelling of NMR spectra. We use kernel ridge regression techniques (least-square support vector regression and linear ridge regression) combined with the smooth overlap of atomic position (SOAP) atom-centered descriptors to efficiently predict NMR interactions: isotropic magnetic shielding and the electric field gradient (EFG) tensor. As illustrated in this work, this approach enables the simulation of MAS and MQMAS NMR spectra of very large models (more than 10000 atoms) and an efficient averaging of NMR properties over MD trajectories of nanoseconds for incorporating finite temperature effects, at computational cost of classical MD simulation. We illustrate these advances on sodium silicate glasses (SiO2-Na2O). NMR parameters (isotropic chemical shift and electric field gradient) could be predicted with an accuracy of 1 to 2% in terms of the total span of the NMR parameter values. To include vibrational effects, an approach is proposed by scaling the EFG tensor in NMR simulations with a factor obtained from the time auto-correlation functions computed on MD trajectory.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"24 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501894","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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