Faraday Discussions最新文献_第5页

Knowledge distillation of neural network potential for molecular crystals 分子晶体神经网络潜能的知识提炼

IF 3.4 3区化学

Faraday Discussions Pub Date : 2024-07-18 DOI: 10.1039/d4fd00090k

Takuya Taniguchi

{"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}

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Crystal structure determination of Verinurad via proton-detected ultra-fast MAS NMR and machine learning 通过质子检测超快 MAS NMR 和机器学习确定 Verinurad 的晶体结构

IF 3.4 3区化学

Faraday Discussions Pub Date : 2024-07-17 DOI: 10.1039/d4fd00076e

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

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Atomic-level structure of the amorphous drug Atuliflapon by NMR crystallography 通过核磁共振晶体学研究无定形药物阿托利夫拉朋的原子级结构

IF 3.4 3区化学

Faraday Discussions Pub Date : 2024-07-17 DOI: 10.1039/d4fd00078a

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

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Nanoscale visualization of the anti-tumor effect of a plasma-activated Ringer’s lactate solution 等离子激活的林格乳酸盐溶液抗肿瘤效果的纳米级可视化

IF 3.4 3区化学

Faraday Discussions Pub Date : 2024-07-16 DOI: 10.1039/d4fd00116h

Junichi Usuda, Kenshin Yagyu, Hiromasa Tanaka, Masaru Hori, Kenji Ishikawa, Takahashi Yasufumi

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Embedding human knowledge in material screening pipeline as filters to identify novel synthesizable inorganic materials 将人类知识嵌入材料筛选管道，作为识别新型可合成无机材料的过滤器

IF 3.4 3区化学

Faraday Discussions Pub Date : 2024-07-16 DOI: 10.1039/d4fd00120f

Basita Das, Kangyu Ji, FANG SHENG, Kyle McCall, Tonio Buonassisi

{"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}

引用次数: 0

Exploring the crystallisation of aspirin in a confined porous material using solid-state nuclear magnetic resonance 利用固态核磁共振探索阿司匹林在密闭多孔材料中的结晶过程

IF 3.4 3区化学

Faraday Discussions Pub Date : 2024-07-16 DOI: 10.1039/d4fd00123k

Marie Juramy, Eric Besson, Stephane Gastaldi, Fabio Ziarelli, Stéphane Viel, Giulia Mollica, Pierre Thureau

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Scanning electrochemical probe microscopy: towards the characterization of micro-and nanostructured photocatalytic materials 扫描电化学探针显微镜：表征微纳米结构的光催化材料

IF 3.4 3区化学

Faraday Discussions Pub Date : 2024-07-15 DOI: 10.1039/d4fd00136b

Giada Caniglia, Sarah Horn, Christine Kranz

{"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}

引用次数: 0

How big is Big Data? 大数据有多大？

IF 3.4 3区化学

Faraday Discussions Pub Date : 2024-07-11 DOI: 10.1039/d4fd00102h

Daniel Speckhard, Tim Bechtel, Luca M. Ghiringhelli, Martin Kuban, Santiago Rigamonti, Claudia Draxl

引用次数: 0

Seeing nanoscale electrocatalytic reactions at individual MoS2 particles under an optical microscope: probing sub-mM oxygen reduction reaction 在光学显微镜下观察单个 MoS2 颗粒的纳米级电催化反应：探测亚毫微米级的氧还原反应

IF 3.4 3区化学

Faraday Discussions Pub Date : 2024-07-10 DOI: 10.1039/d4fd00132j

Nikan Afsahi, Zhu Zhang, Sanli Faez, Jean-Marc Noël, Manas Ranjan Panda, Mainak Majumder, Naimeh Naseritaheri, Jean-François Lemineur, Frederic Kanoufi

{"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}

引用次数: 0

Optical materials discovery and design via federated databases and machine learning 通过联合数据库和机器学习发现和设计光学材料

IF 3.4 3区化学

Faraday Discussions Pub Date : 2024-07-10 DOI: 10.1039/d4fd00092g

Victor Trinquet, Matthew Evans, Cameron Hargreaves, Pierre-Paul De Breuck, Gian-Marco Rignanese

{"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}

引用次数: 0