Jonathan J. Foley, Jonathan F. McTague, A. Eugene DePrince
Polariton chemistry exploits the strong interaction between quantized excitations in molecules and quantized photon states in optical cavities to affect chemical reactivity. Molecular polaritons have been experimentally realized by the coupling of electronic, vibrational, and rovibrational transitions to photon modes, which has spurred a tremendous theoretical effort to model and explain how polariton formation can influence chemistry. This tutorial review focuses on computational approaches for the electronic strong coupling problem through the combination of familiar techniques from ab initio electronic structure theory and cavity quantum electrodynamics, toward the goal of supplying predictive theories for polariton chemistry. Our aim is to emphasize the relevant theoretical details with enough clarity for newcomers to the field to follow, and to present simple and practical code examples to catalyze further development work.
{"title":"<i>Ab initio</i> methods for polariton chemistry","authors":"Jonathan J. Foley, Jonathan F. McTague, A. Eugene DePrince","doi":"10.1063/5.0167243","DOIUrl":"https://doi.org/10.1063/5.0167243","url":null,"abstract":"Polariton chemistry exploits the strong interaction between quantized excitations in molecules and quantized photon states in optical cavities to affect chemical reactivity. Molecular polaritons have been experimentally realized by the coupling of electronic, vibrational, and rovibrational transitions to photon modes, which has spurred a tremendous theoretical effort to model and explain how polariton formation can influence chemistry. This tutorial review focuses on computational approaches for the electronic strong coupling problem through the combination of familiar techniques from ab initio electronic structure theory and cavity quantum electrodynamics, toward the goal of supplying predictive theories for polariton chemistry. Our aim is to emphasize the relevant theoretical details with enough clarity for newcomers to the field to follow, and to present simple and practical code examples to catalyze further development work.","PeriodicalId":72559,"journal":{"name":"Chemical physics reviews","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136098243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The discovery of lytic polysaccharide monooxygenases (LPMOs) as monocopper enzymes for the oxidative cleavage of glycosidic bonds in recalcitrant polysaccharides has revolutionized our understanding of enzymatic biomass conversion. In recent years, the debate regarding whether LPMOs function as monooxygenases or peroxygenases has generated significant interest due to its implications for understanding the mechanisms involved in LPMO-mediated lignocellulosic biomass conversion. This review provides a comprehensive analysis of theoretical calculations and kinetic studies, offering a detailed examination of the catalytic mechanism of LPMOs from a physicochemical perspective. By reviewing theoretical investigations focused on the activation of O2/H2O2 and its impact on LPMO monooxygenase/peroxygenase activity, this review aims to inspire novel insight and innovative approaches for exploring the intricate mechanism of LPMOs.
{"title":"Insight into the peroxygenase activity of lytic polysaccharide monooxygenases (LPMO): Recent progress and mechanistic understanding","authors":"Wa Gao, Heng Yin","doi":"10.1063/5.0161517","DOIUrl":"https://doi.org/10.1063/5.0161517","url":null,"abstract":"The discovery of lytic polysaccharide monooxygenases (LPMOs) as monocopper enzymes for the oxidative cleavage of glycosidic bonds in recalcitrant polysaccharides has revolutionized our understanding of enzymatic biomass conversion. In recent years, the debate regarding whether LPMOs function as monooxygenases or peroxygenases has generated significant interest due to its implications for understanding the mechanisms involved in LPMO-mediated lignocellulosic biomass conversion. This review provides a comprehensive analysis of theoretical calculations and kinetic studies, offering a detailed examination of the catalytic mechanism of LPMOs from a physicochemical perspective. By reviewing theoretical investigations focused on the activation of O2/H2O2 and its impact on LPMO monooxygenase/peroxygenase activity, this review aims to inspire novel insight and innovative approaches for exploring the intricate mechanism of LPMOs.","PeriodicalId":72559,"journal":{"name":"Chemical physics reviews","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134916047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The use of visible light to enable small molecule synthesis has grown substantially over the last 15 years. While much of the focus has been on the development of new methods, mechanistic and kinetic studies can provide valuable information about reaction steps and highlight directions for optimization and new methods. This review focuses on reports of visible light, homogenous photoredox reactions that emphasize direct observation of reaction intermediates and/or contain a significant focus on mechanistic and kinetic studies. How these types of studies can improve reaction yields and rates are highlighted. Finally, reaction quantum yields for over 200 photoredox reactions are summarized for the first time. This often-neglected reaction parameter provides valuable insights into the efficiency of photoredox reactions as well as the clues to the underlying mechanism.
{"title":"Mechanistic and kinetic studies of visible light photoredox reactions","authors":"Eric D. Talbott, Nora L. Burnett, John R. Swierk","doi":"10.1063/5.0156850","DOIUrl":"https://doi.org/10.1063/5.0156850","url":null,"abstract":"The use of visible light to enable small molecule synthesis has grown substantially over the last 15 years. While much of the focus has been on the development of new methods, mechanistic and kinetic studies can provide valuable information about reaction steps and highlight directions for optimization and new methods. This review focuses on reports of visible light, homogenous photoredox reactions that emphasize direct observation of reaction intermediates and/or contain a significant focus on mechanistic and kinetic studies. How these types of studies can improve reaction yields and rates are highlighted. Finally, reaction quantum yields for over 200 photoredox reactions are summarized for the first time. This often-neglected reaction parameter provides valuable insights into the efficiency of photoredox reactions as well as the clues to the underlying mechanism.","PeriodicalId":72559,"journal":{"name":"Chemical physics reviews","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134916989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Neurons, which are made of biological tissue, exhibit cognitive properties that can be replicated in various material substrates. To create brain-inspired computational artificial systems, we can construct microscopic electronic neurons that mimic natural systems. In this paper, we discuss the essential material and device properties needed for a spiking neuron, which can be characterized using impedance spectroscopy and small perturbation equivalent circuit elements. We find that the minimal neuron system requires a capacitor, a chemical inductor, and a negative resistance. These components can be integrated naturally in the physical response of the device, instead of built from separate circuit elements. We identify the structural conditions for smooth oscillations that depend on certain dynamics of a conducting system with internal state variables. These state variables can be of diverse physical nature, such as properties of fluids, electronic solids, or ionic organic materials, implying that functional neurons can be built in various ways. We highlight the importance of detecting the Hopf bifurcation, a critical point in achieving spiking behavior, through spectral features of the impedance. To this end, we provide a systematic method of analysis in terms of the critical characteristic frequencies that can be obtained from impedance methods. Thus, we propose a methodology to quantify the physical and material properties of devices to produce the dynamic properties of neurons necessary for specific sensory-cognitive tasks. By replicating the essential properties of biological neurons in electronic systems, it may be possible to create brain-inspired computational systems with enhanced capabilities in information processing, pattern recognition, and learning. Additionally, understanding the physical and material properties of neurons can contribute to our knowledge of how biological neurons function and interact in complex neural networks. Overall, this paper presents a novel approach toward building brain-inspired artificial systems and provides insight into the important material and device considerations for achieving spiking behavior in electronic neurons.
{"title":"Device physics recipe to make spiking neurons","authors":"Juan Bisquert","doi":"10.1063/5.0145391","DOIUrl":"https://doi.org/10.1063/5.0145391","url":null,"abstract":"Neurons, which are made of biological tissue, exhibit cognitive properties that can be replicated in various material substrates. To create brain-inspired computational artificial systems, we can construct microscopic electronic neurons that mimic natural systems. In this paper, we discuss the essential material and device properties needed for a spiking neuron, which can be characterized using impedance spectroscopy and small perturbation equivalent circuit elements. We find that the minimal neuron system requires a capacitor, a chemical inductor, and a negative resistance. These components can be integrated naturally in the physical response of the device, instead of built from separate circuit elements. We identify the structural conditions for smooth oscillations that depend on certain dynamics of a conducting system with internal state variables. These state variables can be of diverse physical nature, such as properties of fluids, electronic solids, or ionic organic materials, implying that functional neurons can be built in various ways. We highlight the importance of detecting the Hopf bifurcation, a critical point in achieving spiking behavior, through spectral features of the impedance. To this end, we provide a systematic method of analysis in terms of the critical characteristic frequencies that can be obtained from impedance methods. Thus, we propose a methodology to quantify the physical and material properties of devices to produce the dynamic properties of neurons necessary for specific sensory-cognitive tasks. By replicating the essential properties of biological neurons in electronic systems, it may be possible to create brain-inspired computational systems with enhanced capabilities in information processing, pattern recognition, and learning. Additionally, understanding the physical and material properties of neurons can contribute to our knowledge of how biological neurons function and interact in complex neural networks. Overall, this paper presents a novel approach toward building brain-inspired artificial systems and provides insight into the important material and device considerations for achieving spiking behavior in electronic neurons.","PeriodicalId":72559,"journal":{"name":"Chemical physics reviews","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135685822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Machine learning (ML) continues to revolutionize computational chemistry for accelerating predictions and simulations by training on experimental or accurate but expensive quantum mechanical (QM) calculations. Photodynamics simulations require hundreds of trajectories coupled with multiconfigurational QM calculations of excited-state potential energies surfaces that contribute to the prohibitive computational cost at long timescales and complex organic molecules. ML accelerates photodynamics simulations by combining nonadiabatic photodynamics simulations with an ML model trained with high-fidelity QM calculations of energies, forces, and non-adiabatic couplings. This approach has provided time-dependent molecular structural information for understanding photochemical reaction mechanisms of organic reactions in vacuum and complex environments (i.e., explicit solvation). This review focuses on the fundamentals of QM calculations and ML techniques. We, then, discuss the strategies to balance adequate training data and the computational cost of generating these training data. Finally, we demonstrate the power of applying these ML-photodynamics simulations to understand the origin of reactivities and selectivities of organic photochemical reactions, such as cis–trans isomerization, [2 + 2]-cycloaddition, 4π-electrostatic ring-closing, and hydrogen roaming mechanism.
{"title":"Machine learning accelerated photodynamics simulations","authors":"Jingbai Li, Steven A. Lopez","doi":"10.1063/5.0159247","DOIUrl":"https://doi.org/10.1063/5.0159247","url":null,"abstract":"Machine learning (ML) continues to revolutionize computational chemistry for accelerating predictions and simulations by training on experimental or accurate but expensive quantum mechanical (QM) calculations. Photodynamics simulations require hundreds of trajectories coupled with multiconfigurational QM calculations of excited-state potential energies surfaces that contribute to the prohibitive computational cost at long timescales and complex organic molecules. ML accelerates photodynamics simulations by combining nonadiabatic photodynamics simulations with an ML model trained with high-fidelity QM calculations of energies, forces, and non-adiabatic couplings. This approach has provided time-dependent molecular structural information for understanding photochemical reaction mechanisms of organic reactions in vacuum and complex environments (i.e., explicit solvation). This review focuses on the fundamentals of QM calculations and ML techniques. We, then, discuss the strategies to balance adequate training data and the computational cost of generating these training data. Finally, we demonstrate the power of applying these ML-photodynamics simulations to understand the origin of reactivities and selectivities of organic photochemical reactions, such as cis–trans isomerization, [2 + 2]-cycloaddition, 4π-electrostatic ring-closing, and hydrogen roaming mechanism.","PeriodicalId":72559,"journal":{"name":"Chemical physics reviews","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135298901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Engsvang, H. Wu, Y. Knattrup, J. Kubečka, A. Buchgraitz Jensen, J. Elm
Atmospheric molecular cluster formation is the first stage toward aerosol particle formation. Despite intensive progress in recent years, the relative role of different vapors and the mechanisms for forming clusters is still not well-understood. Quantum chemical (QC) methods can give insight into the cluster formation mechanisms and thereby yield information about the potentially relevant compounds. Here, we summarize the QC literature on clustering involving species such as sulfuric acid, methanesulfonic acid, and nitric acid. The importance of iodine species such as iodous acid (HIO2) and iodic acid (HIO3) in atmospheric cluster formation is an emerging topic, and we critically review the recent literature and give our view on how to progress in the future. We outline how machine learning (ML) methods can be used to enhance cluster configurational sampling, leading to a massive increase in the cluster compositions that can be modeled. In the future, ML-boosted cluster formation could allow us to comprehensively understand complex cluster formation with multiple pathways, leading us one step closer to implementing accurate cluster formation mechanisms in atmospheric models.
{"title":"Quantum chemical modeling of atmospheric molecular clusters involving inorganic acids and methanesulfonic acid","authors":"M. Engsvang, H. Wu, Y. Knattrup, J. Kubečka, A. Buchgraitz Jensen, J. Elm","doi":"10.1063/5.0152517","DOIUrl":"https://doi.org/10.1063/5.0152517","url":null,"abstract":"Atmospheric molecular cluster formation is the first stage toward aerosol particle formation. Despite intensive progress in recent years, the relative role of different vapors and the mechanisms for forming clusters is still not well-understood. Quantum chemical (QC) methods can give insight into the cluster formation mechanisms and thereby yield information about the potentially relevant compounds. Here, we summarize the QC literature on clustering involving species such as sulfuric acid, methanesulfonic acid, and nitric acid. The importance of iodine species such as iodous acid (HIO2) and iodic acid (HIO3) in atmospheric cluster formation is an emerging topic, and we critically review the recent literature and give our view on how to progress in the future. We outline how machine learning (ML) methods can be used to enhance cluster configurational sampling, leading to a massive increase in the cluster compositions that can be modeled. In the future, ML-boosted cluster formation could allow us to comprehensively understand complex cluster formation with multiple pathways, leading us one step closer to implementing accurate cluster formation mechanisms in atmospheric models.","PeriodicalId":72559,"journal":{"name":"Chemical physics reviews","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134916044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jiao Wang, Ruiyan Liu, Zhen-Zhen Shen, Jian-Xin Tian, R. Wen
Metal anodes are considered promising candidates for next-generation rechargeable batteries owing to their high theoretical specific capacities. However, practical applications are limited by safety concerns and poor electrochemical performance caused by unstable solid electrolyte interphase (SEI) and uncontrolled metal deposition at the metal anode/electrolyte interface. An in-depth understanding of the interfacial reactions is of vital significance for the development of metal anode-based batteries. In situ electrochemical atomic force microscopy (EC-AFM) enabling high spatial resolution imaging and multifunctional detection is widely used to monitor electrode/electrolyte interfaces in working batteries. In this review, we summarize recent advances in the application of in situ EC-AFM for metal anode processes, including SEI formation and the deposition/dissolution processes of metallic lithium, magnesium, and zinc in metal anode-based batteries, which are conducive to the optimization of metal anodes in energy storage batteries.
{"title":"Recent progress in the application of in situ atomic force microscopy for metal anode processes in energy storage batteries","authors":"Jiao Wang, Ruiyan Liu, Zhen-Zhen Shen, Jian-Xin Tian, R. Wen","doi":"10.1063/5.0100062","DOIUrl":"https://doi.org/10.1063/5.0100062","url":null,"abstract":"Metal anodes are considered promising candidates for next-generation rechargeable batteries owing to their high theoretical specific capacities. However, practical applications are limited by safety concerns and poor electrochemical performance caused by unstable solid electrolyte interphase (SEI) and uncontrolled metal deposition at the metal anode/electrolyte interface. An in-depth understanding of the interfacial reactions is of vital significance for the development of metal anode-based batteries. In situ electrochemical atomic force microscopy (EC-AFM) enabling high spatial resolution imaging and multifunctional detection is widely used to monitor electrode/electrolyte interfaces in working batteries. In this review, we summarize recent advances in the application of in situ EC-AFM for metal anode processes, including SEI formation and the deposition/dissolution processes of metallic lithium, magnesium, and zinc in metal anode-based batteries, which are conducive to the optimization of metal anodes in energy storage batteries.","PeriodicalId":72559,"journal":{"name":"Chemical physics reviews","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48297712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Developing environmentally friendly, sustainable, and biocompatible artificial light-harvesting systems has become an essential area of research to understand natural light-harvesting processes involving multistep resonance energy transfer and building efficient energy conversion systems related to energy and optoelectronic applications. In this direction, bio-scaffolded artificial energy transfer systems for panchromatic light collection and sequential energy transfer have fascinated the scientific community. In this review, we have discussed what the dynamic structure and intrinsic physical properties of biomolecules like deoxyribonucleic acid, proteins, and peptides can provide for the development of new optical devices, sustainable and environmentally friendly white emitting materials, and cascaded energy transfer systems for energy harvesting from light. In doing so, we have highlighted some of the recent advances in bio-scaffolds as a platform for the assembly of different types of donor–acceptor chromophores involved in fluorescence energy transfer.
{"title":"Bio-templated energy transfer system for constructing artificial light-harvesting antennae, white light generation, and photonic nanowires","authors":"Srikrishna Pramanik, S. Mukherjee","doi":"10.1063/5.0163152","DOIUrl":"https://doi.org/10.1063/5.0163152","url":null,"abstract":"Developing environmentally friendly, sustainable, and biocompatible artificial light-harvesting systems has become an essential area of research to understand natural light-harvesting processes involving multistep resonance energy transfer and building efficient energy conversion systems related to energy and optoelectronic applications. In this direction, bio-scaffolded artificial energy transfer systems for panchromatic light collection and sequential energy transfer have fascinated the scientific community. In this review, we have discussed what the dynamic structure and intrinsic physical properties of biomolecules like deoxyribonucleic acid, proteins, and peptides can provide for the development of new optical devices, sustainable and environmentally friendly white emitting materials, and cascaded energy transfer systems for energy harvesting from light. In doing so, we have highlighted some of the recent advances in bio-scaffolds as a platform for the assembly of different types of donor–acceptor chromophores involved in fluorescence energy transfer.","PeriodicalId":72559,"journal":{"name":"Chemical physics reviews","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41711317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Eui-Cheol Shin, Ji-Ho Mun, Seungil Baek, Jaegwan Jung, Yong-Hyun Kim
Triboelectricity has long been discussed from ancient Greece to modern times in daily life experiences as well as in the earliest stages of planet formation and the cutting-edge triboelectric nanogenerator technology. Despite the numerous efforts from scientists and engineers, fundamental understanding of the friction-driven static electrification has remained elusive. Here, we review recent progress in understanding the microscopic origin of triboelectricity, directly associated with frictional energy dissipation at the interface, from mechanochemistry, strain-driven polarization, and tribo-tunneling to thermoelectricity. Noticeably, we note that the microscopic thermoelectric charging mechanism due to interfacial frictional heat offers a generally applicable, but exactly solvable triboelectric model in the weakly interacting regime, implying many opportunities in triboelectric based science and technology in the future.
{"title":"Recent progress in understanding the microscopic origin of triboelectricity from mechanochemistry to thermoelectricity","authors":"Eui-Cheol Shin, Ji-Ho Mun, Seungil Baek, Jaegwan Jung, Yong-Hyun Kim","doi":"10.1063/5.0147372","DOIUrl":"https://doi.org/10.1063/5.0147372","url":null,"abstract":"Triboelectricity has long been discussed from ancient Greece to modern times in daily life experiences as well as in the earliest stages of planet formation and the cutting-edge triboelectric nanogenerator technology. Despite the numerous efforts from scientists and engineers, fundamental understanding of the friction-driven static electrification has remained elusive. Here, we review recent progress in understanding the microscopic origin of triboelectricity, directly associated with frictional energy dissipation at the interface, from mechanochemistry, strain-driven polarization, and tribo-tunneling to thermoelectricity. Noticeably, we note that the microscopic thermoelectric charging mechanism due to interfacial frictional heat offers a generally applicable, but exactly solvable triboelectric model in the weakly interacting regime, implying many opportunities in triboelectric based science and technology in the future.","PeriodicalId":72559,"journal":{"name":"Chemical physics reviews","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48533228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Metal–organic frameworks (MOFs) have premium exceptional properties for a variety of functions, such as gas separation and storage and catalysis. The large variety of possible inorganometallic nodes and organic linkers provide an almost unlimited number of combinations for assembling MOFs, which makes the experimental characterization and examination of all potentially useful combinations practically impossible. Furthermore, experimental studies of MOFs typically fall short in uncovering crucial details regarding their mechanisms of action or the molecular details responsible for their functional properties, such as the nature of adsorbate binding or the structures of transition states. Computational modeling has, therefore, become an efficient and important tool for strategizing the functionalization of MOFs and explicating the mechanisms of their functions. Here, we review the computational methodologies used for computational studies of MOFs, especially Kohn–Sham density functional theory and combined quantum mechanical and molecular mechanical methods for calculating their structural, electronic, and magnetic properties, as well as for understanding the mechanisms of MOFs' applications to magetic devices, thermal conduction, gas adsorption, separation, storage, and sensing, thermal catalysis, photocatalysis, and electrocatalysis.
{"title":"Computational quantum chemistry of metal–organic frameworks","authors":"Indrani Choudhuri, Jingyun Ye, D. Truhlar","doi":"10.1063/5.0153656","DOIUrl":"https://doi.org/10.1063/5.0153656","url":null,"abstract":"Metal–organic frameworks (MOFs) have premium exceptional properties for a variety of functions, such as gas separation and storage and catalysis. The large variety of possible inorganometallic nodes and organic linkers provide an almost unlimited number of combinations for assembling MOFs, which makes the experimental characterization and examination of all potentially useful combinations practically impossible. Furthermore, experimental studies of MOFs typically fall short in uncovering crucial details regarding their mechanisms of action or the molecular details responsible for their functional properties, such as the nature of adsorbate binding or the structures of transition states. Computational modeling has, therefore, become an efficient and important tool for strategizing the functionalization of MOFs and explicating the mechanisms of their functions. Here, we review the computational methodologies used for computational studies of MOFs, especially Kohn–Sham density functional theory and combined quantum mechanical and molecular mechanical methods for calculating their structural, electronic, and magnetic properties, as well as for understanding the mechanisms of MOFs' applications to magetic devices, thermal conduction, gas adsorption, separation, storage, and sensing, thermal catalysis, photocatalysis, and electrocatalysis.","PeriodicalId":72559,"journal":{"name":"Chemical physics reviews","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44499963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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