Pub Date : 2021-05-05DOI: 10.1146/ANNUREV-MATSCI-092320-102133
H. Evans, Lingling Mao, R. Seshadri, A. Cheetham
Successful strategies for the design of crystalline materials with useful function are frequently based on the systematic tuning of chemical composition within a given structural family. Perovskites with the formula ABX3, perhaps the best-known example of such a family, have a vast range of elements on A, B, and X sites, which are associated with a similarly vast range of functionality. Layered double perovskites (LDPs), a subset of this family, are obtained by suitable slicing and restacking of the perovskite structure, with the additional design feature of ordered cations and/or anions. In addition to inorganic LDPs, we also discuss hybrid (organic-inorganic) LDPs here, where the A-site cation is a protonated organic amine. Several examples of inorganic LDPs are presented with a discussion of their ferroic, magnetic, and optical properties. The emerging area of hybrid LDPs is particularly rich and is leading to exciting discoveries of new compounds with unique structures and fascinating optoelectronic properties. We provide context for what is important to consider when designing new materials and conclude with a discussion of future opportunities in the broad LDP area.
{"title":"Layered Double Perovskites","authors":"H. Evans, Lingling Mao, R. Seshadri, A. Cheetham","doi":"10.1146/ANNUREV-MATSCI-092320-102133","DOIUrl":"https://doi.org/10.1146/ANNUREV-MATSCI-092320-102133","url":null,"abstract":"Successful strategies for the design of crystalline materials with useful function are frequently based on the systematic tuning of chemical composition within a given structural family. Perovskites with the formula ABX3, perhaps the best-known example of such a family, have a vast range of elements on A, B, and X sites, which are associated with a similarly vast range of functionality. Layered double perovskites (LDPs), a subset of this family, are obtained by suitable slicing and restacking of the perovskite structure, with the additional design feature of ordered cations and/or anions. In addition to inorganic LDPs, we also discuss hybrid (organic-inorganic) LDPs here, where the A-site cation is a protonated organic amine. Several examples of inorganic LDPs are presented with a discussion of their ferroic, magnetic, and optical properties. The emerging area of hybrid LDPs is particularly rich and is leading to exciting discoveries of new compounds with unique structures and fascinating optoelectronic properties. We provide context for what is important to consider when designing new materials and conclude with a discussion of future opportunities in the broad LDP area.","PeriodicalId":8055,"journal":{"name":"Annual Review of Materials Research","volume":null,"pages":null},"PeriodicalIF":9.7,"publicationDate":"2021-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75921393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-13DOI: 10.1146/ANNUREV-MATSCI-091520-011838
C. Chiatti, C. Fabiani, A. Pisello
In recent decades, research on persistent luminescence has led to new phosphors and promising performance. Efforts to improve the quality of phosphors’ afterglow have paved the way toward innovativ...
{"title":"Long Persistent Luminescence: A Road Map Toward Promising Future Developments in Energy and Environmental Science","authors":"C. Chiatti, C. Fabiani, A. Pisello","doi":"10.1146/ANNUREV-MATSCI-091520-011838","DOIUrl":"https://doi.org/10.1146/ANNUREV-MATSCI-091520-011838","url":null,"abstract":"In recent decades, research on persistent luminescence has led to new phosphors and promising performance. Efforts to improve the quality of phosphors’ afterglow have paved the way toward innovativ...","PeriodicalId":8055,"journal":{"name":"Annual Review of Materials Research","volume":null,"pages":null},"PeriodicalIF":9.7,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79600797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-13DOI: 10.1146/ANNUREV-MATSCI-080619-111402
Aristide Gumyusenge, A. Melianas, S. Keene, A. Salleo
Neuromorphic computing is becoming increasingly prominent as artificial intelligence (AI) facilitates progressively seamless interaction between humans and machines. The conventional von Neumann architecture and complementary metal-oxide-semiconductor transistor scaling are unable to meet the highly demanding computational density and energy efficiency requirements of AI. Neuromorphic computing aims to address these challenges by using brain-like computing architectures and novel synaptic memories that coallocate information storage and computation, thereby enabling low latency at high energy efficiency and high memory density. Though various emerging memory devices have been extensively studied to emulate the functionality of biological synapses, there is currently no material/device system that encompasses both the needed metrics for high-performance neuromorphic computing and the required biocompatibility for potential body-computer integration. In this review, we aim to equip the reader with general design principles and materials requirements for realizing high-performance organic neuromorphic devices. We use instructive examples from recent literature to discuss each requirement, illustrating the challenges as well as future research opportunities. Though organic devices still face many challenges to become major players in neuromorphic computing, mostly due to their lack of compliance with back-end-of-line processes required for integration with digital logic, we propose that their biocompatibility and mechanical conformability give them an advantage for creating adaptive biointerfaces, brain-machine interfaces, and biology-inspired prosthetics.
{"title":"Materials Strategies for Organic Neuromorphic Devices","authors":"Aristide Gumyusenge, A. Melianas, S. Keene, A. Salleo","doi":"10.1146/ANNUREV-MATSCI-080619-111402","DOIUrl":"https://doi.org/10.1146/ANNUREV-MATSCI-080619-111402","url":null,"abstract":"Neuromorphic computing is becoming increasingly prominent as artificial intelligence (AI) facilitates progressively seamless interaction between humans and machines. The conventional von Neumann architecture and complementary metal-oxide-semiconductor transistor scaling are unable to meet the highly demanding computational density and energy efficiency requirements of AI. Neuromorphic computing aims to address these challenges by using brain-like computing architectures and novel synaptic memories that coallocate information storage and computation, thereby enabling low latency at high energy efficiency and high memory density. Though various emerging memory devices have been extensively studied to emulate the functionality of biological synapses, there is currently no material/device system that encompasses both the needed metrics for high-performance neuromorphic computing and the required biocompatibility for potential body-computer integration. In this review, we aim to equip the reader with general design principles and materials requirements for realizing high-performance organic neuromorphic devices. We use instructive examples from recent literature to discuss each requirement, illustrating the challenges as well as future research opportunities. Though organic devices still face many challenges to become major players in neuromorphic computing, mostly due to their lack of compliance with back-end-of-line processes required for integration with digital logic, we propose that their biocompatibility and mechanical conformability give them an advantage for creating adaptive biointerfaces, brain-machine interfaces, and biology-inspired prosthetics.","PeriodicalId":8055,"journal":{"name":"Annual Review of Materials Research","volume":null,"pages":null},"PeriodicalIF":9.7,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77328161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-23DOI: 10.1146/ANNUREV-MATSCI-070720-013445
A. Voskanyan, A. Navrotsky
A renaissance of interest in crystallographic shear structures and our recent work in this remarkable class of materials inspired this review. We first summarize the geometrical aspects of shear pl...
{"title":"Shear Pleasure: The Structure, Formation, and Thermodynamics of Crystallographic Shear Phases","authors":"A. Voskanyan, A. Navrotsky","doi":"10.1146/ANNUREV-MATSCI-070720-013445","DOIUrl":"https://doi.org/10.1146/ANNUREV-MATSCI-070720-013445","url":null,"abstract":"A renaissance of interest in crystallographic shear structures and our recent work in this remarkable class of materials inspired this review. We first summarize the geometrical aspects of shear pl...","PeriodicalId":8055,"journal":{"name":"Annual Review of Materials Research","volume":null,"pages":null},"PeriodicalIF":9.7,"publicationDate":"2021-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75345676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-09DOI: 10.1146/ANNUREV-MATSCI-080819-012808
Eve M. Mozur, J. Neilson
Hybrid halide perovskite semiconductors exhibit complex, dynamical disorder while also harboring properties ideal for optoelectronic applications that include photovoltaics. However, these materials are structurally and compositionally distinct from traditional compound semiconductors composed of tetrahedrally coordinated elements with an average valence electron count of silicon. The additional dynamic degrees of freedom of hybrid halide perovskites underlie many of their potentially transformative physical properties. Neutron scattering and spectroscopy studies of the atomic dynamics of these materials have yielded significant insights into their functional properties. Specifically, inelastic neutron scattering has been used to elucidate the phonon band structure, and quasi-elastic neutron scattering has revealed the nature of the uncorrelated dynamics pertaining to molecular reorientations. Understanding the dynamics of these complex semiconductors has elucidated the temperature-dependent phase stability and origins of defect-tolerant electronic transport from the highly polarizable dielectric response. Furthermore, the dynamic degrees of freedom of the hybrid perovskites provide additional opportunities for application engineering and innovation.
{"title":"Cation Dynamics in Hybrid Halide Perovskites","authors":"Eve M. Mozur, J. Neilson","doi":"10.1146/ANNUREV-MATSCI-080819-012808","DOIUrl":"https://doi.org/10.1146/ANNUREV-MATSCI-080819-012808","url":null,"abstract":"Hybrid halide perovskite semiconductors exhibit complex, dynamical disorder while also harboring properties ideal for optoelectronic applications that include photovoltaics. However, these materials are structurally and compositionally distinct from traditional compound semiconductors composed of tetrahedrally coordinated elements with an average valence electron count of silicon. The additional dynamic degrees of freedom of hybrid halide perovskites underlie many of their potentially transformative physical properties. Neutron scattering and spectroscopy studies of the atomic dynamics of these materials have yielded significant insights into their functional properties. Specifically, inelastic neutron scattering has been used to elucidate the phonon band structure, and quasi-elastic neutron scattering has revealed the nature of the uncorrelated dynamics pertaining to molecular reorientations. Understanding the dynamics of these complex semiconductors has elucidated the temperature-dependent phase stability and origins of defect-tolerant electronic transport from the highly polarizable dielectric response. Furthermore, the dynamic degrees of freedom of the hybrid perovskites provide additional opportunities for application engineering and innovation.","PeriodicalId":8055,"journal":{"name":"Annual Review of Materials Research","volume":null,"pages":null},"PeriodicalIF":9.7,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82380659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-15DOI: 10.1146/annurev-matsci-080819-012444
Ann L. Greenaway, C. Melamed, M. Tellekamp, R. Woods‐Robinson, E. Toberer, J. Neilson, A. Tamboli
Interest in inorganic ternary nitride materials has grown rapidly over the past few decades, as their diverse chemistries and structures make them appealing for a variety of applications. Due to synthetic challenges posed by the stability of N2, the number of predicted nitride compounds dwarfs the number that has been synthesized, offering a breadth of opportunity for exploration. This review summarizes the fundamental properties and structural chemistry of ternary nitrides, leveraging metastability and the impact of nitrogen chemical potential. A discussion of prevalent defects, both detrimental and beneficial, is followed by a survey of synthesis techniques and their interplay with metastability. Throughout the review, we highlight applications (such as solid-state lighting, electrochemical energy storage, and electronic devices) in which ternary nitrides show particular promise.
{"title":"Ternary Nitride Materials: Fundamentals and Emerging Device Applications","authors":"Ann L. Greenaway, C. Melamed, M. Tellekamp, R. Woods‐Robinson, E. Toberer, J. Neilson, A. Tamboli","doi":"10.1146/annurev-matsci-080819-012444","DOIUrl":"https://doi.org/10.1146/annurev-matsci-080819-012444","url":null,"abstract":"Interest in inorganic ternary nitride materials has grown rapidly over the past few decades, as their diverse chemistries and structures make them appealing for a variety of applications. Due to synthetic challenges posed by the stability of N2, the number of predicted nitride compounds dwarfs the number that has been synthesized, offering a breadth of opportunity for exploration. This review summarizes the fundamental properties and structural chemistry of ternary nitrides, leveraging metastability and the impact of nitrogen chemical potential. A discussion of prevalent defects, both detrimental and beneficial, is followed by a survey of synthesis techniques and their interplay with metastability. Throughout the review, we highlight applications (such as solid-state lighting, electrochemical energy storage, and electronic devices) in which ternary nitrides show particular promise.","PeriodicalId":8055,"journal":{"name":"Annual Review of Materials Research","volume":null,"pages":null},"PeriodicalIF":9.7,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89706551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1146/annurev-matsci-110519-094700
Taylor D. Sparks, Steven K. Kauwe, M. Parry, Aria Mansouri Tehrani, Jakoah Brgoch
The development of structural materials with outstanding mechanical response has long been sought for innumerable industrial, technological, and even biomedical applications. However, these compoun...
{"title":"Machine Learning for Structural Materials","authors":"Taylor D. Sparks, Steven K. Kauwe, M. Parry, Aria Mansouri Tehrani, Jakoah Brgoch","doi":"10.1146/annurev-matsci-110519-094700","DOIUrl":"https://doi.org/10.1146/annurev-matsci-110519-094700","url":null,"abstract":"The development of structural materials with outstanding mechanical response has long been sought for innumerable industrial, technological, and even biomedical applications. However, these compoun...","PeriodicalId":8055,"journal":{"name":"Annual Review of Materials Research","volume":null,"pages":null},"PeriodicalIF":9.7,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81307835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1146/annurev-matsci-090319-010954
J. Saal, A. Oliynyk, B. Meredig
The rapidly growing interest in machine learning (ML) for materials discovery has resulted in a large body of published work. However, only a small fraction of these publications includes confirmat...
{"title":"Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches","authors":"J. Saal, A. Oliynyk, B. Meredig","doi":"10.1146/annurev-matsci-090319-010954","DOIUrl":"https://doi.org/10.1146/annurev-matsci-090319-010954","url":null,"abstract":"The rapidly growing interest in machine learning (ML) for materials discovery has resulted in a large body of published work. However, only a small fraction of these publications includes confirmat...","PeriodicalId":8055,"journal":{"name":"Annual Review of Materials Research","volume":null,"pages":null},"PeriodicalIF":9.7,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77970196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1146/annurev-matsci-081519-011844
Zhaodong Chu, Lu Zheng, K. Lai
Understanding the nanoscale electrodynamic properties of a material at microwave frequencies is of great interest for materials science, condensed matter physics, device engineering, and biology. With specialized probes, sensitive detection electronics, and improved scanning platforms, microwave microscopy has become an important tool for cutting-edge materials research in the past decade. In this article, we review the basic components and data interpretation of microwave imaging and its broad range of applications. In addition to the general-purpose mapping of permittivity and conductivity, microwave microscopy is now exploited to perform quantitative measurements on semiconductor devices, photosensitive materials, ferroelectric domains and domain walls, and acoustic-wave systems. Implementation of the technique in low-temperature and high-magnetic-field chambers has also led to major discoveries in quantum materials with strong correlation and topological order. We conclude the review with an outlook of the ultimate resolution, operation frequency, and future industrial and academic applications of near-field microwave microscopy.
{"title":"Microwave Microscopy and Its Applications","authors":"Zhaodong Chu, Lu Zheng, K. Lai","doi":"10.1146/annurev-matsci-081519-011844","DOIUrl":"https://doi.org/10.1146/annurev-matsci-081519-011844","url":null,"abstract":"Understanding the nanoscale electrodynamic properties of a material at microwave frequencies is of great interest for materials science, condensed matter physics, device engineering, and biology. With specialized probes, sensitive detection electronics, and improved scanning platforms, microwave microscopy has become an important tool for cutting-edge materials research in the past decade. In this article, we review the basic components and data interpretation of microwave imaging and its broad range of applications. In addition to the general-purpose mapping of permittivity and conductivity, microwave microscopy is now exploited to perform quantitative measurements on semiconductor devices, photosensitive materials, ferroelectric domains and domain walls, and acoustic-wave systems. Implementation of the technique in low-temperature and high-magnetic-field chambers has also led to major discoveries in quantum materials with strong correlation and topological order. We conclude the review with an outlook of the ultimate resolution, operation frequency, and future industrial and academic applications of near-field microwave microscopy.","PeriodicalId":8055,"journal":{"name":"Annual Review of Materials Research","volume":null,"pages":null},"PeriodicalIF":9.7,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1146/annurev-matsci-081519-011844","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72512063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1146/annurev-matsci-082019-105100
C. Suh, Clyde Fare, J. Warren, Edward O. Pyzer-Knapp
Machine learning, applied to chemical and materials data, is transforming the field of materials discovery and design, yet significant work is still required to fully take advantage of machine learning algorithms, tools, and methods. Here, we review the accomplishments to date of the community and assess the maturity of state-of-the-art, data-intensive research activities that combine perspectives from materials science and chemistry. We focus on three major themes—learning to see, learning to estimate, and learning to search materials—to show how advanced computational learning technologies are rapidly and successfully used to solve materials and chemistry problems. Additionally, we discuss a clear path toward a future where data-driven approaches to materials discovery and design are standard practice.
{"title":"Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery","authors":"C. Suh, Clyde Fare, J. Warren, Edward O. Pyzer-Knapp","doi":"10.1146/annurev-matsci-082019-105100","DOIUrl":"https://doi.org/10.1146/annurev-matsci-082019-105100","url":null,"abstract":"Machine learning, applied to chemical and materials data, is transforming the field of materials discovery and design, yet significant work is still required to fully take advantage of machine learning algorithms, tools, and methods. Here, we review the accomplishments to date of the community and assess the maturity of state-of-the-art, data-intensive research activities that combine perspectives from materials science and chemistry. We focus on three major themes—learning to see, learning to estimate, and learning to search materials—to show how advanced computational learning technologies are rapidly and successfully used to solve materials and chemistry problems. Additionally, we discuss a clear path toward a future where data-driven approaches to materials discovery and design are standard practice.","PeriodicalId":8055,"journal":{"name":"Annual Review of Materials Research","volume":null,"pages":null},"PeriodicalIF":9.7,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83460546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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