Precision Chemistry最新文献_第7页

Single-Molecule Bioelectronic Sensors with AI-Aided Data Analysis: Convergence and Challenges 单分子生物电子传感器与人工智能辅助数据分析：融合与挑战

Precision Chemistry Pub Date : 2024-09-16 DOI: 10.1021/prechem.4c0004810.1021/prechem.4c00048

Yuxin Yang, Yueqi Li, Longhua Tang* and Jinghong Li*,

{"title":"Single-Molecule Bioelectronic Sensors with AI-Aided Data Analysis: Convergence and Challenges","authors":"Yuxin Yang, Yueqi Li, Longhua Tang* and Jinghong Li*, ","doi":"10.1021/prechem.4c0004810.1021/prechem.4c00048","DOIUrl":"https://doi.org/10.1021/prechem.4c00048https://doi.org/10.1021/prechem.4c00048","url":null,"abstract":"Single-molecule bioelectronic sensing, a groundbreaking domain in biological research, has revolutionized our understanding of molecules by revealing deep insights into fundamental biological processes. The advent of emergent technologies, such as nanogapped electrodes and nanopores, has greatly enhanced this field, providing exceptional sensitivity, resolution, and integration capabilities. However, challenges persist, such as complex data sets with high noise levels and stochastic molecular dynamics. Artificial intelligence (AI) has stepped in to address these issues with its powerful data processing capabilities. AI algorithms effectively extract meaningful features, detect subtle changes, improve signal-to-noise ratios, and uncover hidden patterns in massive data. This review explores the synergy between AI and single-molecule bioelectronic sensing, focusing on how AI enhances signal processing and data analysis to boost accuracy and reliability. We also discuss current limitations and future directions for integrating AI, highlighting its potential to advance biological research and technological innovation.","PeriodicalId":29793,"journal":{"name":"Precision Chemistry","volume":"2 10","pages":"518–538 518–538"},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/prechem.4c00048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142517492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LASP to the Future of Atomic Simulation: Intelligence and Automation. 从LASP到原子模拟的未来：智能和自动化。

Precision Chemistry Pub Date : 2024-09-14 eCollection Date: 2024-12-23 DOI: 10.1021/prechem.4c00060

Xin-Tian Xie, Zheng-Xin Yang, Dongxiao Chen, Yun-Fei Shi, Pei-Lin Kang, Sicong Ma, Ye-Fei Li, Cheng Shang, Zhi-Pan Liu

{"title":"LASP to the Future of Atomic Simulation: Intelligence and Automation.","authors":"Xin-Tian Xie, Zheng-Xin Yang, Dongxiao Chen, Yun-Fei Shi, Pei-Lin Kang, Sicong Ma, Ye-Fei Li, Cheng Shang, Zhi-Pan Liu","doi":"10.1021/prechem.4c00060","DOIUrl":"10.1021/prechem.4c00060","url":null,"abstract":"Atomic simulations aim to understand and predict complex physical phenomena, the success of which relies largely on the accuracy of the potential energy surface description and the efficiency to capture important rare events. LASP software (large-scale atomic simulation with a Neural Network Potential), released in 2018, incorporates the key ingredients to fulfill the ultimate goal of atomic simulations by combining advanced neural network potentials with efficient global optimization methods. This review introduces the recent development of the software along two main streams, namely, higher intelligence and more automation, to solve complex material and reaction problems. The latest version of LASP (LASP 3.7) features the global many-body function corrected neural network (G-MBNN) to improve the PES accuracy with low cost, which achieves a linear scaling efficiency for large-scale atomic simulations. The key functionalities of LASP are updated to incorporate (i) the ASOP and ML-interface methods for finding complex surface and interface structures under grand canonic conditions; (ii) the ML-TS and MMLPS methods to identify the lowest energy reaction pathway. With these powerful functionalities, LASP now serves as an intelligent data generator to create computational databases for end users. We exemplify the recent LASP database construction in zeolite and the metal-ligand properties for a new catalyst design.","PeriodicalId":29793,"journal":{"name":"Precision Chemistry","volume":"2 12","pages":"612-627"},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11672538/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142903804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LASP to the Future of Atomic Simulation: Intelligence and Automation 从LASP到原子模拟的未来：智能和自动化

Precision Chemistry Pub Date : 2024-09-14 DOI: 10.1021/prechem.4c0006010.1021/prechem.4c00060

Xin-Tian Xie, Zheng-Xin Yang, Dongxiao Chen, Yun-Fei Shi, Pei-Lin Kang, Sicong Ma, Ye-Fei Li, Cheng Shang* and Zhi-Pan Liu*,

{"title":"LASP to the Future of Atomic Simulation: Intelligence and Automation","authors":"Xin-Tian Xie, Zheng-Xin Yang, Dongxiao Chen, Yun-Fei Shi, Pei-Lin Kang, Sicong Ma, Ye-Fei Li, Cheng Shang* and Zhi-Pan Liu*, ","doi":"10.1021/prechem.4c0006010.1021/prechem.4c00060","DOIUrl":"https://doi.org/10.1021/prechem.4c00060https://doi.org/10.1021/prechem.4c00060","url":null,"abstract":"Atomic simulations aim to understand and predict complex physical phenomena, the success of which relies largely on the accuracy of the potential energy surface description and the efficiency to capture important rare events. LASP software (large-scale atomic simulation with a Neural Network Potential), released in 2018, incorporates the key ingredients to fulfill the ultimate goal of atomic simulations by combining advanced neural network potentials with efficient global optimization methods. This review introduces the recent development of the software along two main streams, namely, higher intelligence and more automation, to solve complex material and reaction problems. The latest version of LASP (LASP 3.7) features the global many-body function corrected neural network (G-MBNN) to improve the PES accuracy with low cost, which achieves a linear scaling efficiency for large-scale atomic simulations. The key functionalities of LASP are updated to incorporate (i) the ASOP and ML-interface methods for finding complex surface and interface structures under grand canonic conditions; (ii) the ML-TS and MMLPS methods to identify the lowest energy reaction pathway. With these powerful functionalities, LASP now serves as an intelligent data generator to create computational databases for end users. We exemplify the recent LASP database construction in zeolite and the metal–ligand properties for a new catalyst design.","PeriodicalId":29793,"journal":{"name":"Precision Chemistry","volume":"2 12","pages":"612–627 612–627"},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/prechem.4c00060","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging Machine Learning Potentials for In-Situ Searching of Active sites in Heterogeneous Catalysis 利用机器学习潜力原位搜索异相催化中的活性位点

Precision Chemistry Pub Date : 2024-09-11 DOI: 10.1021/prechem.4c0005110.1021/prechem.4c00051

Xiran Cheng, Chenyu Wu, Jiayan Xu, Yulan Han, Wenbo Xie* and P. Hu*,

{"title":"Leveraging Machine Learning Potentials for In-Situ Searching of Active sites in Heterogeneous Catalysis","authors":"Xiran Cheng, Chenyu Wu, Jiayan Xu, Yulan Han, Wenbo Xie* and P. Hu*, ","doi":"10.1021/prechem.4c0005110.1021/prechem.4c00051","DOIUrl":"https://doi.org/10.1021/prechem.4c00051https://doi.org/10.1021/prechem.4c00051","url":null,"abstract":"This Perspective explores the integration of machine learning potentials (MLPs) in the research of heterogeneous catalysis, focusing on their role in identifying in situ active sites and enhancing the understanding of catalytic processes. MLPs utilize extensive databases from high-throughput density functional theory (DFT) calculations to train models that predict atomic configurations, energies, and forces with near-DFT accuracy. These capabilities allow MLPs to handle significantly larger systems and extend simulation times beyond the limitations of traditional ab initio methods. Coupled with global optimization algorithms, MLPs enable systematic investigations across vast structural spaces, making substantial contributions to the modeling of catalyst surface structures under reactive conditions. The review aims to provide a broad introduction to recent advancements and practical guidance on employing MLPs and also showcases several exemplary cases of MLP-driven discoveries related to surface structure changes under reactive conditions and the nature of active sites in heterogeneous catalysis. The prevailing challenges faced by this approach are also discussed.","PeriodicalId":29793,"journal":{"name":"Precision Chemistry","volume":"2 11","pages":"570–586 570–586"},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/prechem.4c00051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142694624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging Machine Learning Potentials for In-Situ Searching of Active sites in Heterogeneous Catalysis. 利用机器学习潜力在多相催化中原位搜索活性位点。

Precision Chemistry Pub Date : 2024-09-11 eCollection Date: 2024-11-25 DOI: 10.1021/prechem.4c00051

Xiran Cheng, Chenyu Wu, Jiayan Xu, Yulan Han, Wenbo Xie, P Hu

{"title":"Leveraging Machine Learning Potentials for In-Situ Searching of Active sites in Heterogeneous Catalysis.","authors":"Xiran Cheng, Chenyu Wu, Jiayan Xu, Yulan Han, Wenbo Xie, P Hu","doi":"10.1021/prechem.4c00051","DOIUrl":"10.1021/prechem.4c00051","url":null,"abstract":"This Perspective explores the integration of machine learning potentials (MLPs) in the research of heterogeneous catalysis, focusing on their role in identifying in situ active sites and enhancing the understanding of catalytic processes. MLPs utilize extensive databases from high-throughput density functional theory (DFT) calculations to train models that predict atomic configurations, energies, and forces with near-DFT accuracy. These capabilities allow MLPs to handle significantly larger systems and extend simulation times beyond the limitations of traditional ab initio methods. Coupled with global optimization algorithms, MLPs enable systematic investigations across vast structural spaces, making substantial contributions to the modeling of catalyst surface structures under reactive conditions. The review aims to provide a broad introduction to recent advancements and practical guidance on employing MLPs and also showcases several exemplary cases of MLP-driven discoveries related to surface structure changes under reactive conditions and the nature of active sites in heterogeneous catalysis. The prevailing challenges faced by this approach are also discussed.","PeriodicalId":29793,"journal":{"name":"Precision Chemistry","volume":"2 11","pages":"570-586"},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11600352/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142751744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Precision Chemistry for Two-Dimensional Materials. 二维材料的精密化学。

Precision Chemistry Pub Date : 2024-08-26 DOI: 10.1021/prechem.4c00065

Xiangfeng Duan

引用次数: 0

Precision Chemistry for Two-Dimensional Materials 二维材料的精密化学

Precision Chemistry Pub Date : 2024-08-26 DOI: 10.1021/prechem.4c0006510.1021/prechem.4c00065

Xiangfeng Duan*,

引用次数: 0

The Coupling of Synthesis and Electrochemistry to Enable the Reversible Storage of Hydrogen as Metal Hydrides 将合成与电化学结合起来，以实现氢在金属氢化物中的可逆储存

Precision Chemistry Pub Date : 2024-08-24 DOI: 10.1021/prechem.4c0003010.1021/prechem.4c00030

Matthew Nava*, Lina M. Zarnitsa and Martin-Louis Y. Riu,

{"title":"The Coupling of Synthesis and Electrochemistry to Enable the Reversible Storage of Hydrogen as Metal Hydrides","authors":"Matthew Nava*, Lina M. Zarnitsa and Martin-Louis Y. Riu, ","doi":"10.1021/prechem.4c0003010.1021/prechem.4c00030","DOIUrl":"https://doi.org/10.1021/prechem.4c00030https://doi.org/10.1021/prechem.4c00030","url":null,"abstract":"Given its high gravimetric energy density and status as a clean fuel when derived from renewables, hydrogen (H2) is considered a premier candidate for energy storage; however, its low volumetric density limits its broader application. Chemical storage through the reversible incorporation of H2 into chemical bonds offers a promising solution to its low volumetric density, circumventing subpar energy densities and substantial infrastructure investments associated with physical storage methods. Metal hydrides are promising candidates for chemical storage because of their high gravimetric capacity and tunability through nanostructuring and alloying. Moreover, metal hydride/H2 interconversion may be interfaced with electrochemistry, which offers potential solutions to some of the challenges associated with traditional thermochemical platforms. In this Perspective, we describe anticipated challenges associated with electrochemically mediated metal hydride/H2 interconversion, including thermodynamic efficiencies of metal hydride formation, sluggish kinetics, and electrode passivation. Additionally, we propose potential solutions to these problems through the design of molecular mediators that may control factors such as metal hydride solubility, particle morphology, and hydride affinity. Realization of an electrochemically mediated metal hydride/H2 interconversion platform introduces new tools to address challenges associated with hydrogen storage platforms and contributes toward the development of room-temperature hydrogen storage platforms.","PeriodicalId":29793,"journal":{"name":"Precision Chemistry","volume":"2 11","pages":"563–569 563–569"},"PeriodicalIF":0.0,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/prechem.4c00030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142694394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Coupling of Synthesis and Electrochemistry to Enable the Reversible Storage of Hydrogen as Metal Hydrides. 氢作为金属氢化物可逆储存的合成与电化学耦合研究。

Precision Chemistry Pub Date : 2024-08-24 eCollection Date: 2024-11-25 DOI: 10.1021/prechem.4c00030

Matthew Nava, Lina M Zarnitsa, Martin-Louis Y Riu

{"title":"The Coupling of Synthesis and Electrochemistry to Enable the Reversible Storage of Hydrogen as Metal Hydrides.","authors":"Matthew Nava, Lina M Zarnitsa, Martin-Louis Y Riu","doi":"10.1021/prechem.4c00030","DOIUrl":"10.1021/prechem.4c00030","url":null,"abstract":"Given its high gravimetric energy density and status as a clean fuel when derived from renewables, hydrogen (H2) is considered a premier candidate for energy storage; however, its low volumetric density limits its broader application. Chemical storage through the reversible incorporation of H2 into chemical bonds offers a promising solution to its low volumetric density, circumventing subpar energy densities and substantial infrastructure investments associated with physical storage methods. Metal hydrides are promising candidates for chemical storage because of their high gravimetric capacity and tunability through nanostructuring and alloying. Moreover, metal hydride/H2 interconversion may be interfaced with electrochemistry, which offers potential solutions to some of the challenges associated with traditional thermochemical platforms. In this Perspective, we describe anticipated challenges associated with electrochemically mediated metal hydride/H2 interconversion, including thermodynamic efficiencies of metal hydride formation, sluggish kinetics, and electrode passivation. Additionally, we propose potential solutions to these problems through the design of molecular mediators that may control factors such as metal hydride solubility, particle morphology, and hydride affinity. Realization of an electrochemically mediated metal hydride/H2 interconversion platform introduces new tools to address challenges associated with hydrogen storage platforms and contributes toward the development of room-temperature hydrogen storage platforms.","PeriodicalId":29793,"journal":{"name":"Precision Chemistry","volume":"2 11","pages":"563-569"},"PeriodicalIF":0.0,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11600345/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142751755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Molecular Interactions in Atomically Precise Metal Nanoclusters. 原子精度金属纳米团簇中的分子相互作用。

Precision Chemistry Pub Date : 2024-08-23 eCollection Date: 2024-10-28 DOI: 10.1021/prechem.4c00044

Jing Qian, Zhucheng Yang, Jingkuan Lyu, Qiaofeng Yao, Jianping Xie

{"title":"Molecular Interactions in Atomically Precise Metal Nanoclusters.","authors":"Jing Qian, Zhucheng Yang, Jingkuan Lyu, Qiaofeng Yao, Jianping Xie","doi":"10.1021/prechem.4c00044","DOIUrl":"10.1021/prechem.4c00044","url":null,"abstract":"For nanochemistry, precise manipulation of nanoscale structures and the accompanying chemical properties at atomic precision is one of the greatest challenges today. The scientific community strives to develop and design customized nanomaterials, while molecular interactions often serve as key tools or probes for this atomically precise undertaking. In this Perspective, metal nanoclusters, especially gold nanoclusters, serve as a good platform for understanding such nanoscale interactions. These nanoclusters often have a core size of about 2 nm, a defined number of core metal atoms, and protecting ligands with known crystal structure. The atomically precise structure of metal nanoclusters allows us to discuss how the molecular interactions facilitate the systematic modification and functionalization of nanoclusters from their inner core, through the ligand shell, to the external assembly. Interestingly, the atomic packing structure of the nanocluster core can be affected by forces on the surface. After discussing the core structure, we examine various atomic-level strategies to enhance their photoluminescent quantum yield and improve nanoclusters' catalytic performance. Beyond the single cluster level, various attractive or repulsive molecular interactions have been employed to engineer the self-assembly behavior and thus packing morphology of metal nanoclusters. The methodological and fundamental insights systemized in this review should be useful for customizing the cluster structure and assembly patterns at the atomic level.","PeriodicalId":29793,"journal":{"name":"Precision Chemistry","volume":"2 10","pages":"495-517"},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11522999/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142558985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0