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 the 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.
{"title":"Knowledge distillation of neural network potential for molecular crystals","authors":"Takuya Taniguchi","doi":"10.1039/d4fd00090k","DOIUrl":"https://doi.org/10.1039/d4fd00090k","url":null,"abstract":"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 the soft targets, <em>i.e.</em>, 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.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"80 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141737507","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}
Daria Torodii, Jacob Holmes, Pinelopi Moutzouri, Sten O. Nilsson Lill, Manuel Cordova, Arthur C. Pinon, Kristof Grohe, Sebastian Wegner, Okky Dwichandra Putra, Stefan Tommy Norberg, Anette Welinder, Staffan Schantz, Lyndon Emsley
The recent development of ultra-fast 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.
{"title":"Crystal structure determination of Verinurad via proton-detected ultra-fast MAS NMR and machine learning","authors":"Daria Torodii, Jacob Holmes, Pinelopi Moutzouri, Sten O. Nilsson Lill, Manuel Cordova, Arthur C. Pinon, Kristof Grohe, Sebastian Wegner, Okky Dwichandra Putra, Stefan Tommy Norberg, Anette Welinder, Staffan Schantz, Lyndon Emsley","doi":"10.1039/d4fd00076e","DOIUrl":"https://doi.org/10.1039/d4fd00076e","url":null,"abstract":"The recent development of ultra-fast 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 <small><sup>1</sup></small>H resolution improvement at ultra-fast MAS and use solely <small><sup>1</sup></small>H-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 <small><sup>13</sup></small>C-detected experiments, without the need to employ dynamic nuclear polarization.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"21 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141719549","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}
Jacob Holmes, Daria Torodii, Martins Balodis, Manuel Cordova, Albert Hofstetter, Federico Paruzzo, Sten O. Nilsson Lill, Emma Eriksson, Pierrick Berruyer, Bruno Simões de Almeida, Michael J. Quayle, Stefan Tommy Norberg, Anna Svensk-Ankarberg, Staffan Schantz, Lyndon Emsley
We determine the complete atomic-level structure of the amorphous form of the drug altuliflapon, a 5-lipooxygenase activating protein (FLAP) inhibitor, by 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.
{"title":"Atomic-level structure of the amorphous drug Atuliflapon by NMR crystallography","authors":"Jacob Holmes, Daria Torodii, Martins Balodis, Manuel Cordova, Albert Hofstetter, Federico Paruzzo, Sten O. Nilsson Lill, Emma Eriksson, Pierrick Berruyer, Bruno Simões de Almeida, Michael J. Quayle, Stefan Tommy Norberg, Anna Svensk-Ankarberg, Staffan Schantz, Lyndon Emsley","doi":"10.1039/d4fd00078a","DOIUrl":"https://doi.org/10.1039/d4fd00078a","url":null,"abstract":"We determine the complete atomic-level structure of the amorphous form of the drug altuliflapon, a 5-lipooxygenase activating protein (FLAP) inhibitor, by 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.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"38 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141719550","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}
Plasma-activated Ringer’s lactate solutions (PALs), which are Ringer’s lactate solutions treated with non-thermal atmospheric-pressure plasma, have anti-tumor effect and can be used for chemotherapy. As the anti-tumor effect of the PAL is influenced by the cell-treatment time, it is necessary to monitor the structural changes of the cell surface with non-invasive, nanoscale, and time-lapse imaging to understand the anti-tumor effect. In this study, to characterize the anti-tumor effect of the PAL, we used a scanning ion conductance microscopy (SICM), using glass nanopipettes as probes, to visualize the structural changes of the cell surface. SICM time-lapse topographic imaging visualized a decrease in the movement of lamellipodia in normal cells and cancer cells after the PAL treatment. Furthermore, in normal cells, protrusive structures were observed on the cell surface. Time-lapse imaging using SICM allowed us to characterize the differences in the morphological changes between the normal and cancer cells upon exposure to the PAL.
等离子体激活的林格氏乳酸盐溶液（PALs）是用非热大气压等离子体处理过的林格氏乳酸盐溶液，具有抗肿瘤作用，可用于化疗。由于 PAL 的抗肿瘤效果受细胞处理时间的影响，因此有必要利用无创、纳米级和延时成像技术监测细胞表面的结构变化，以了解其抗肿瘤效果。在本研究中，为了表征 PAL 的抗肿瘤效果，我们使用了扫描离子电导显微镜（SICM），以玻璃纳米吸头为探针，观察细胞表面的结构变化。SICM 延时地形图成像显示，PAL 处理后，正常细胞和癌细胞中的片状突起运动均有所减少。此外，在正常细胞中，还观察到细胞表面有突起结构。利用 SICM 的延时成像技术，我们得以描述正常细胞和癌细胞在接触 PAL 后形态变化的差异。
{"title":"Nanoscale visualization of the anti-tumor effect of a plasma-activated Ringer’s lactate solution","authors":"Junichi Usuda, Kenshin Yagyu, Hiromasa Tanaka, Masaru Hori, Kenji Ishikawa, Takahashi Yasufumi","doi":"10.1039/d4fd00116h","DOIUrl":"https://doi.org/10.1039/d4fd00116h","url":null,"abstract":"Plasma-activated Ringer’s lactate solutions (PALs), which are Ringer’s lactate solutions treated with non-thermal atmospheric-pressure plasma, have anti-tumor effect and can be used for chemotherapy. As the anti-tumor effect of the PAL is influenced by the cell-treatment time, it is necessary to monitor the structural changes of the cell surface with non-invasive, nanoscale, and time-lapse imaging to understand the anti-tumor effect. In this study, to characterize the anti-tumor effect of the PAL, we used a scanning ion conductance microscopy (SICM), using glass nanopipettes as probes, to visualize the structural changes of the cell surface. SICM time-lapse topographic imaging visualized a decrease in the movement of lamellipodia in normal cells and cancer cells after the PAL treatment. Furthermore, in normal cells, protrusive structures were observed on the cell surface. Time-lapse imaging using SICM allowed us to characterize the differences in the morphological changes between the normal and cancer cells upon exposure to the PAL.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"106 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141719551","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}
Basita Das, Kangyu Ji, FANG SHENG, Kyle McCall, Tonio Buonassisi
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.
{"title":"Embedding human knowledge in material screening pipeline as filters to identify novel synthesizable inorganic materials","authors":"Basita Das, Kangyu Ji, FANG SHENG, Kyle McCall, Tonio Buonassisi","doi":"10.1039/d4fd00120f","DOIUrl":"https://doi.org/10.1039/d4fd00120f","url":null,"abstract":"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.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"322 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141719553","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}
Marie Juramy, Eric Besson, Stephane Gastaldi, Fabio Ziarelli, Stéphane Viel, Giulia Mollica, Pierre Thureau
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.
在这项研究中，核磁共振 (NMR) 被用来研究阿司匹林在介孔 SBA-15 硅材料中的结晶行为。此外，还利用专门设计的多孔材料（其壁内含有极化剂）研究了动态核极化（DNP）实验的潜力。当结晶发生在介孔结构的孔隙中时，可观察到 "可转移结晶形式 II "的形成。相反，块状结晶会产生最稳定的阿司匹林形态，即形态 I。值得注意的是，即使在浸渍 30 天后，析晶形式 II 仍被困在介孔 SBA-15 二氧化硅材料的孔隙中，这表明它在这种密闭环境中具有持久的稳定性。
{"title":"Exploring the crystallisation of aspirin in a confined porous material using solid-state nuclear magnetic resonance","authors":"Marie Juramy, Eric Besson, Stephane Gastaldi, Fabio Ziarelli, Stéphane Viel, Giulia Mollica, Pierre Thureau","doi":"10.1039/d4fd00123k","DOIUrl":"https://doi.org/10.1039/d4fd00123k","url":null,"abstract":"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.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"4 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141719552","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}
Platinum-black (Pt-B) has been demonstrated as an excellent electrocatalytic material for the electrochemical oxidation of hydrogen peroxide (H2O2). As Pt-B films can be deposited electrochemically, micro- and nano-sized conductive transducers can be modified with Pt-B. Here, we present the potential of Pt-B micro- and sub-micro-sized sensors for the detection and quantification of hydrogen (H2) in solution. Using these microsensors, no sampling step for H2 determination is required and e.g., in photocatalysis, the onset of H2 evolution can be monitored in situ. We present Pt-B- based H2 micro- and sub-micro-sized sensors based on different electrochemical transducers such as microelectrodes and atomic force microscopy (AFM)- scanning electrochemical microscopy (SECM) probes, which enable local measurements e.g., at heterogenized photocatalytically active samples. The microsensors are characterized in terms of limits of detection (LOD), which ranges from 4.0 µM to 30 µM depending on the size of the sensors and the experimental conditions such as type of electrolyte and pH. The sensors were tested for the in situ H2 evolution by light-driven water-splitting, i.e., using ascorbic acid or triethanolamine, showing a wide linear concertation range, good reproducibility, and high sensitivity. Proof-of-principle experiments using Pt-B-modified cantilever-based sensors were performed using a model sample like platinum substrate to map the electrochemical H2 evolution along with the topography using AFM-SECM.
{"title":"Scanning electrochemical probe microscopy: towards the characterization of micro-and nanostructured photocatalytic materials","authors":"Giada Caniglia, Sarah Horn, Christine Kranz","doi":"10.1039/d4fd00136b","DOIUrl":"https://doi.org/10.1039/d4fd00136b","url":null,"abstract":"Platinum-black (Pt-B) has been demonstrated as an excellent electrocatalytic material for the electrochemical oxidation of hydrogen peroxide (H2O2). As Pt-B films can be deposited electrochemically, micro- and nano-sized conductive transducers can be modified with Pt-B. Here, we present the potential of Pt-B micro- and sub-micro-sized sensors for the detection and quantification of hydrogen (H2) in solution. Using these microsensors, no sampling step for H2 determination is required and e.g., in photocatalysis, the onset of H2 evolution can be monitored in situ. We present Pt-B- based H2 micro- and sub-micro-sized sensors based on different electrochemical transducers such as microelectrodes and atomic force microscopy (AFM)- scanning electrochemical microscopy (SECM) probes, which enable local measurements e.g., at heterogenized photocatalytically active samples. The microsensors are characterized in terms of limits of detection (LOD), which ranges from 4.0 µM to 30 µM depending on the size of the sensors and the experimental conditions such as type of electrolyte and pH. The sensors were tested for the in situ H2 evolution by light-driven water-splitting, i.e., using ascorbic acid or triethanolamine, showing a wide linear concertation range, good reproducibility, and high sensitivity. Proof-of-principle experiments using Pt-B-modified cantilever-based sensors were performed using a model sample like platinum substrate to map the electrochemical H2 evolution along with the topography using AFM-SECM.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"11 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141719554","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}
Daniel Speckhard, Tim Bechtel, Luca M. Ghiringhelli, Martin Kuban, Santiago Rigamonti, Claudia Draxl
Big data has ushered in a new wave of predictive power using machine learning models. In this work, we assess what {it big} means in the context of typical materials-science machine-learning problems. This concerns not only data volume, but also data quality and veracity as much as infrastructure issues. With selected examples, we ask (i) how models generalize to similar datasets, (ii) how high-quality datasets can be gathered from heterogenous sources, (iii) how the feature set and complexity of a model can affect expressivity, and (iv) what infrastructure requirements are needed to create larger datasets and train models on them. In sum, we find that big data present unique challenges along very different aspects that should serve to motivate further work.
{"title":"How big is Big Data?","authors":"Daniel Speckhard, Tim Bechtel, Luca M. Ghiringhelli, Martin Kuban, Santiago Rigamonti, Claudia Draxl","doi":"10.1039/d4fd00102h","DOIUrl":"https://doi.org/10.1039/d4fd00102h","url":null,"abstract":"Big data has ushered in a new wave of predictive power using machine learning models. In this work, we assess what {it big} means in the context of typical materials-science machine-learning problems. This concerns not only data volume, but also data quality and veracity as much as infrastructure issues. With selected examples, we ask (i) how models generalize to similar datasets, (ii) how high-quality datasets can be gathered from heterogenous sources, (iii) how the feature set and complexity of a model can affect expressivity, and (iv) what infrastructure requirements are needed to create larger datasets and train models on them. In sum, we find that big data present unique challenges along very different aspects that should serve to motivate further work.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"61 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141613550","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}
MoS2 is a promising electrocatalytic material for replacing noble metals. Nanoelectrochemistry studies, such as using nanoelectrochemical cell confinement, have particularly helped in demonstrating the preferential electrocatalytic activity of MoS2 edges. These findings have been accompanied by considerable research efforts to synthetize edge-abundant nanomaterials. However, to fully apprehend their electrocatalytic performance, at the single particle level, new instrumental developments are also needed. Here, we feature a highly sensitive refractive index optical microscopy technique, namely interferometric scattering microscopy (iSCAT), for monitoring local electrochemistry at single MoS2 petal-like sub-microparticles. This work focuses on the oxygen reduction reaction (ORR), which operates at low current densities and thus requires high-sensitivity imaging techniques. By employing a precipitation reaction to reveal the ORR activity and utilizing the high spatial resolution and contrast of iSCAT, we achieve the sensitivity required to evaluate the ORR activity at single MoS2 particles.
{"title":"Seeing nanoscale electrocatalytic reactions at individual MoS2 particles under an optical microscope: probing sub-mM oxygen reduction reaction","authors":"Nikan Afsahi, Zhu Zhang, Sanli Faez, Jean-Marc Noël, Manas Ranjan Panda, Mainak Majumder, Naimeh Naseritaheri, Jean-François Lemineur, Frederic Kanoufi","doi":"10.1039/d4fd00132j","DOIUrl":"https://doi.org/10.1039/d4fd00132j","url":null,"abstract":"MoS2 is a promising electrocatalytic material for replacing noble metals. Nanoelectrochemistry studies, such as using nanoelectrochemical cell confinement, have particularly helped in demonstrating the preferential electrocatalytic activity of MoS2 edges. These findings have been accompanied by considerable research efforts to synthetize edge-abundant nanomaterials. However, to fully apprehend their electrocatalytic performance, at the single particle level, new instrumental developments are also needed. Here, we feature a highly sensitive refractive index optical microscopy technique, namely interferometric scattering microscopy (iSCAT), for monitoring local electrochemistry at single MoS2 petal-like sub-microparticles. This work focuses on the oxygen reduction reaction (ORR), which operates at low current densities and thus requires high-sensitivity imaging techniques. By employing a precipitation reaction to reveal the ORR activity and utilizing the high spatial resolution and contrast of iSCAT, we achieve the sensitivity required to evaluate the ORR activity at single MoS2 particles.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"32 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141578049","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}
Victor Trinquet, Matthew Evans, Cameron Hargreaves, Pierre-Paul De Breuck, Gian-Marco Rignanese
Combinatorial and guided screening of materials space with density-functional theory and related approaches has provided a wealth of hypothetical inorganic materials, which are increasingly tabulated in open databases. The OPTIMADE API is a standardised format for representing crystal structures, their measured and computed properties, and the methods for querying and filtering them from remote resources. Currently, the OPTIMADE federation spans over 20 data providers, rendering over 30 million structures accessible in this way, many of which are novel and have only recently been suggested by machine learning-based approaches. In this work, we outline our approach to non-exhaustively screen this dynamic trove of structures for the next-generation of optical materials. By applying MODNet, a neural network-based model for property prediction that has been shown to perform especially well for small materials datasets, within a combined active learning and high-throughput computation framework, we isolate particular structures and chemistries that should be most fruitful for further theoretical calculations and for experimental study as high-refractive-index materials. By making explicit use of automated calculations, federated dataset curation and machine learning, and by releasing these publicly, the workflows presented here can be periodically re-assessed as new databases implement OPTIMADE, and new hypothetical materials are suggested.
{"title":"Optical materials discovery and design via federated databases and machine learning","authors":"Victor Trinquet, Matthew Evans, Cameron Hargreaves, Pierre-Paul De Breuck, Gian-Marco Rignanese","doi":"10.1039/d4fd00092g","DOIUrl":"https://doi.org/10.1039/d4fd00092g","url":null,"abstract":"Combinatorial and guided screening of materials space with density-functional theory and related approaches has provided a wealth of hypothetical inorganic materials, which are increasingly tabulated in open databases. The OPTIMADE API is a standardised format for representing crystal structures, their measured and computed properties, and the methods for querying and filtering them from remote resources. Currently, the OPTIMADE federation spans over 20 data providers, rendering over 30 million structures accessible in this way, many of which are novel and have only recently been suggested by machine learning-based approaches. In this work, we outline our approach to non-exhaustively screen this dynamic trove of structures for the next-generation of optical materials. By applying MODNet, a neural network-based model for property prediction that has been shown to perform especially well for small materials datasets, within a combined active learning and high-throughput computation framework, we isolate particular structures and chemistries that should be most fruitful for further theoretical calculations and for experimental study as high-refractive-index materials. By making explicit use of automated calculations, federated dataset curation and machine learning, and by releasing these publicly, the workflows presented here can be periodically re-assessed as new databases implement OPTIMADE, and new hypothetical materials are suggested.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":"27 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141573174","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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