Current Opinion in Solid State & Materials Science最新文献

{"title":"The path towards plasma facing components: A review of state-of-the-art in W-based refractory high-entropy alloys","authors":"Caleb Hatler ,&nbsp;Ishtiaque Robin ,&nbsp;Hyosim Kim ,&nbsp;Nathan Curtis ,&nbsp;Bochuan Sun ,&nbsp;Eda Aydogan ,&nbsp;Saryu Fensin ,&nbsp;Adrien Couet ,&nbsp;Enrique Martinez ,&nbsp;Dan J. Thoma ,&nbsp;Osman El Atwani","doi":"10.1016/j.cossms.2024.101201","DOIUrl":"10.1016/j.cossms.2024.101201","url":null,"abstract":"<div><div>Developing advanced materials for plasma-facing components (PFCs) in fusion reactors is a crucial aspect for achieving sustained energy production. Tungsten (W) − based refractory high-entropy alloys (RHEAs) have emerged as promising candidates due to their superior radiation tolerance and high-temperature strength. This review paper will focus on recent advancements in W-based RHEA research, with particular emphasis on: predictive modelling with machine learning (ML) to expedite the identification of optimal RHEA compositions; additive manufacturing (AM) techniques, highlighting their advantages for rapid prototyping and high-throughput multi-compositional sample production; mechanical properties relevant to PFC applications, including hardness, high-temperature strength, and ductility; and the radiation tolerance of W-based RHEAs under irradiated conditions. Finally, the key challenges and opportunities for future research, particularly the holistic analysis of candidate compositions as well as the role of radiation activation and oxidation are identified. This review aims to provide a comprehensive overview of W-based RHEAs for fusion applications and their potential to guide the development and validation of advanced refractory high entropy alloys.</div></div>","PeriodicalId":295,"journal":{"name":"Current Opinion in Solid State & Materials Science","volume":"34 ","pages":"Article 101201"},"PeriodicalIF":12.2,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433812","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}

{"title":"Artificial Intelligence and Machine Learning for materials","authors":"Yuebing Zheng","doi":"10.1016/j.cossms.2024.101202","DOIUrl":"10.1016/j.cossms.2024.101202","url":null,"abstract":"","PeriodicalId":295,"journal":{"name":"Current Opinion in Solid State & Materials Science","volume":"34 ","pages":"Article 101202"},"PeriodicalIF":12.2,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142423126","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}

{"title":"Grain refinement and morphological control of intermetallic compounds: A comprehensive review","authors":"Amrit Raj Paul ,&nbsp;Jayshri Dumbre ,&nbsp;Dong Qiu ,&nbsp;Mark Easton ,&nbsp;Maciej Mazur ,&nbsp;Manidipto Mukherjee","doi":"10.1016/j.cossms.2024.101200","DOIUrl":"10.1016/j.cossms.2024.101200","url":null,"abstract":"<div><div>Intermetallic compounds (IMCs) are ordered solid-state compounds formed from chemical reactions between two or more metals exhibiting distinctive crystal arrangements and precise stoichiometric ratios, setting them apart from the matrix of the alloys. In general, IMCs are formed in three configurations: In the form of secondary phase precipitates distributed within the matrix phase, in the form of an IMC alloy, and at the bimetallic interfaces of functionally/transitionally graded structures. However, the IMCs as precipitates in the matrix phase, do not possess many challenges and are often desirable to improve the strength by imparting precipitation hardening. But, in the case of IMC alloys and bimetallic structures, the grain size and morphology of IMCs directly influence the integrity and durability of the developed structure. Given the inherent brittleness of most IMCs, the utilisation of IMCs in critical applications is substantially restricted. In response to this long-standing challenge, there has been extensive research into methods for improving the ductility of IMCs. This review emphasises two key methodologies: solidification-based and non-solidification-based approaches, both aiming to enhance IMC’s mechanical properties either by transitioning large to smaller grain microstructure or dendritic to equiaxed morphology. Solidification-based strategies, including heterogeneous nucleation and external-field-induced morphological alteration like the use of ultrasonic vibration, magnetic, and electric fields, are meticulously evaluated, uncovering research gaps. Non-solidification-based methods like severe plastic deformation and mechanical alloying are critically examined on the suitability of modern manufacturing techniques such as additive manufacturing. Among these, ultrasonic vibration emerges as the most promising for IMCs morphological transformation. Although static magnetic and electric fields exhibit potential, further investigation is required. Despite knowledge gaps, these techniques hold the potential to elevate IMC-containing alloy characteristics. Future research, especially for specific IMC groups and emerging manufacturing processes, is encouraged to propel metallurgical grain refinement or morphological transformation. In addition, the current and emerging application of various IMCs are thoroughly discussed to identify the importance of IMCs in various science and engineering domains. This comprehensive review enhances comprehension of IMC-based grain alteration, paving the way to design advanced materials across various applications.</div></div>","PeriodicalId":295,"journal":{"name":"Current Opinion in Solid State & Materials Science","volume":"33 ","pages":"Article 101200"},"PeriodicalIF":12.2,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Autonomous research and development of structural materials – An introduction and vision","authors":"D.B. Miracle ,&nbsp;D.J. Thoma","doi":"10.1016/j.cossms.2024.101188","DOIUrl":"10.1016/j.cossms.2024.101188","url":null,"abstract":"<div><div>Blending artificial intelligence and automation enables the new field of autonomous research and development for materials science. A recent review of this still new field was evaluated to seek new opportunities, and structural materials were identified as a topic for future growth. A workshop was organized in Denver, CO on 20–22 April 2022 to explore this theme. The results from this workshop are given in this viewpoint set. The present paper describes four new themes introduced to the autonomous research and development field by structural materials: new artificial intelligence methods; a vision for rapid on-demand synthesis (RODS) of bulk (≥100<!--> <!-->gm) metallic and ceramic materials; new methods for measuring properties; and a new synergy between materials development and engineering design. The remaining papers in this viewpoint set present ideas and discussions from the Denver workshop and more in-depth presentations of major workshop themes.</div></div>","PeriodicalId":295,"journal":{"name":"Current Opinion in Solid State & Materials Science","volume":"33 ","pages":"Article 101188"},"PeriodicalIF":12.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427390","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}

{"title":"Monolithic 3D integration as a pathway to energy-efficient computing and beyond: From materials and devices to architectures and chips","authors":"Yijia Fan ,&nbsp;Ran An ,&nbsp;Jianshi Tang,&nbsp;Yijun Li,&nbsp;Ting Liu,&nbsp;Bin Gao,&nbsp;He Qian,&nbsp;Huaqiang Wu","doi":"10.1016/j.cossms.2024.101199","DOIUrl":"10.1016/j.cossms.2024.101199","url":null,"abstract":"<div><div>As emerging technologies like artificial intelligence (AI) and big data continue to evolve, the demand for high-performance computing (HPC) has been increasing, driving the development of computing chips towards greater energy efficiency and multifunctionality. Monolithic 3D integration (M3D) is poised to be a key enabling technology, by vertically stacking multiple functional layers made of backend-of-the-line (BEOL)-compatible devices on top of Si circuits and interconnecting them with high-density interlayer vias (ILVs). Currently, contenders for functional materials and devices in M3D include carbon nanotubes, two-dimensional (2D) materials, oxide semiconductors and a variety of emerging memories, such as resistive random-access memory (RRAM). This article first discusses the key properties and latest research developments of those materials and their device applications. As a representative example, we then review the recent progress on RRAM-based M3D architectures that integrate memory, computing, and other functional elements to facilitate computing-in-memory (CIM). Finally, we further discuss the opportunities and challenges of M3D as a promising pathway to energy-efficient computing.</div></div>","PeriodicalId":295,"journal":{"name":"Current Opinion in Solid State & Materials Science","volume":"33 ","pages":"Article 101199"},"PeriodicalIF":12.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427391","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}

{"title":"SARS-CoV-2 viral remnants and implications for inflammation and post-acute infection sequelae","authors":"Han Fu ,&nbsp;Liyan Zhai ,&nbsp;Hongyu Wang ,&nbsp;Melody M.H. Li ,&nbsp;Gerard C.L. Wong ,&nbsp;Yue Zhang","doi":"10.1016/j.cossms.2024.101191","DOIUrl":"10.1016/j.cossms.2024.101191","url":null,"abstract":"<div><div>At present, we do not understand precisely how the SARS-CoV-2 coronavirus induces a spectrum of immune responses in different infected hosts, including severe inflammation in some, or how post-acute infection sequelae come about. In this review, we consider a conceptual framework whereby the virus itself is a reservoir of peptide motifs with pro-inflammatory activity. These motifs can potentially be liberated by highly variable proteolytic processing by the host. We focus on the ability of viral peptide motifs that can mimic innate immune peptides (more commonly known as ‘antimicrobial peptides’ (AMPs)). AMPs (and their ‘xenoAMP’ mimics) are not themselves pathogen-associated molecular patterns (PAMPs) that activate innate immunity via recognition by host pattern recognition receptors (PRRs) but can strongly amplify PRR activation via promoting multivalent PAMP presentation. An important mechanism in the host’s immune amplification machinery and is implicated in a range of autoimmune conditions, including lupus and rheumatoid arthritis, which are among the sequelae of COVID-19. We review experiments that show AMPs and SARS-CoV-2-derived xenoAMP can assemble with PAMPs such as dsRNA into pro-inflammatory complexes, resulting in cooperative, multivalent immune recognition by PRRs and grossly amplified inflammatory responses, a phenomenon generally not observed in harmless coronavirus homologs. We also review the persistence of viral remnants from other viral infections and their association with inflammatory sequelae long after the infection has been cleared.</div></div>","PeriodicalId":295,"journal":{"name":"Current Opinion in Solid State & Materials Science","volume":"33 ","pages":"Article 101191"},"PeriodicalIF":12.2,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315828","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}

{"title":"Machine learning in materials research: Developments over the last decade and challenges for the future","authors":"Anubhav Jain","doi":"10.1016/j.cossms.2024.101189","DOIUrl":"10.1016/j.cossms.2024.101189","url":null,"abstract":"<div><p>The number of studies that apply machine learning (ML) to materials science has been growing at a rate of approximately 1.67 times per year over the past decade. In this review, I examine this growth in various contexts. First, I present an analysis of the most commonly used tools (software, databases, materials science methods, and ML methods) used within papers that apply ML to materials science. The analysis demonstrates that despite the growth of deep learning techniques, the use of classical machine learning is still dominant as a whole. It also demonstrates how new research can effectively build upon past research, particular in the domain of ML models trained on density functional theory calculation data. Next, I present the progression of best scores as a function of time on the matbench materials science benchmark for formation enthalpy prediction. In particular, a dramatic improvement of 7 times reduction in error is obtained when progressing from feature-based methods that use conventional ML (random forest, support vector regression, <em>etc.</em>) to the use of graph neural network techniques. Finally, I provide views on future challenges and opportunities, focusing on data size and complexity, extrapolation, interpretation, access, and relevance.</p></div>","PeriodicalId":295,"journal":{"name":"Current Opinion in Solid State & Materials Science","volume":"33 ","pages":"Article 101189"},"PeriodicalIF":12.2,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S135902862400055X/pdfft?md5=daf1f5860dd3d81b7ae5c13746fc62e9&pid=1-s2.0-S135902862400055X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142168392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prospects and challenges of electrochemical random-access memory for deep-learning accelerators","authors":"Jinsong Cui ,&nbsp;Haoran Liu ,&nbsp;Qing Cao","doi":"10.1016/j.cossms.2024.101187","DOIUrl":"10.1016/j.cossms.2024.101187","url":null,"abstract":"<div><p>The ever-expanding capabilities of machine learning are powered by exponentially growing complexity of deep neural network (DNN) models, requiring more energy and chip-area efficient hardware to carry out increasingly computational expensive model-inference and training tasks. Electrochemical random-access memories (ECRAMs) are developed specifically to implement efficient analog in-memory computing for these data-intensive workloads, showing some critical advantages over competing memory technologies mostly developed originally for digital electronics. ECRAMs possess the distinctive capability to switch between a very large number of memristive states with a high level of symmetry, small cycle-to-cycle variability, and low energy consumption; and they simultaneously exhibit good endurance, long data retention, fast switching speed up to nanoseconds, and verified scalability down to sub-50 nm regime, therefore holding great promise in realizing deep-learning accelerators when heterogeneously integrated with silicon-based peripheral circuits. In this review, we first examine challenges in constructing in-memory-computing accelerators and unique advantages of ECRAMs. We then critically assess the various ionic species, channel materials, and solid-state electrolytes employed in ECRAMs that influence device programming characteristics and performance metrics with their different memristive modulation and ionic transport mechanisms. Furthermore, ECRAM device engineering and integration schemes are discussed, within the context of their implementation in high-density pseudo-crossbar array microarchitectures for performing DNN inference and training with high parallelism. Finally, we offer our insights regarding major remaining obstacles and emerging opportunities of harnessing ECRAMs to realize deep-learning accelerators through material-device-circuit-architecture-algorithm co-design.</p></div>","PeriodicalId":295,"journal":{"name":"Current Opinion in Solid State & Materials Science","volume":"32 ","pages":"Article 101187"},"PeriodicalIF":12.2,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1359028624000536/pdfft?md5=33fbb3f4ff0a27b0a69d3fcaa7f064ea&pid=1-s2.0-S1359028624000536-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142136393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electric current-induced phenomena in metallic materials","authors":"Moon-Jo Kim ,&nbsp;Tu-Anh Bui-Thi ,&nbsp;Sung-Gyu Kang ,&nbsp;Sung-Tae Hong ,&nbsp;Heung Nam Han","doi":"10.1016/j.cossms.2024.101190","DOIUrl":"10.1016/j.cossms.2024.101190","url":null,"abstract":"<div><p>The application of electric current on metallic materials alters the microstructures and mechanical properties of materials. The improved formability and accelerated microstructural evolution in material via the application of electric current is referred to as electric current-induced phenomena. This review includes extensive experimental and computational studies on the deformation behavior and microstructural evolutions of metallic materials, underlying mechanisms, and practical applications in industry. We precisely introduce various electric current-induced effects by considering different materials and electric conditions. The discussion covers the mechanisms underlying these effects, emphasizing both thermal and athermal effects of electric current, supported by experimental evidence, physical principles, atomic-scale simulations, and numerical methods. Furthermore, we explore the applications of electric current-induced phenomena in material processing techniques including electrically-assisted forming, treatment, joining, and machining. This review aims to deepen the understanding of how electric currents affect metallic materials and inspire further development of advanced fabrication and processing technologies in time- and energy-efficient ways.</p></div>","PeriodicalId":295,"journal":{"name":"Current Opinion in Solid State & Materials Science","volume":"32 ","pages":"Article 101190"},"PeriodicalIF":12.2,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169410","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}

{"title":"Autonomous materials research and design: Characterization","authors":"Kevin Kaufmann ,&nbsp;Kenneth S. Vecchio","doi":"10.1016/j.cossms.2024.101192","DOIUrl":"10.1016/j.cossms.2024.101192","url":null,"abstract":"<div><p>New materials are a fundamental component of most major advancements in human history. The pivotal role materials play in the development of next generation technologies has spurred campaigns such as the Materials Genome Initiative (MGI) with the goal of reducing the time and cost to discover, characterize, and deploy advanced materials. As goals of the MGI have been met and new capabilities have emerged, a contemporary vision has taken shape within the scientific community whereby the exploration of materials space is dramatically accelerated by artificial intelligence agent(s) capable of performing research independently from humans and achieving a paradigm change in the field. As this idea comes to fruition and new materials are more rapidly computationally evaluated and synthesized nearly on demand, the rate at which a complete characterization of each candidate material’s properties can be completed and understood within the context of all other potential solutions will be the next bottleneck in a materials design campaign. This work provides an overview of the technical and conceptual components related to materials characterization discussed during a workshop dedicated to challenging the way materials research is thought of and performed within the emergent field of autonomous materials research and design (AMRAD). Furthermore, general considerations for developing autonomous characterization are presented along with related works and a discussion of their progress and shortcomings toward the AMRAD vision.</p></div>","PeriodicalId":295,"journal":{"name":"Current Opinion in Solid State & Materials Science","volume":"32 ","pages":"Article 101192"},"PeriodicalIF":12.2,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1359028624000585/pdfft?md5=befbb7ffbbdc18d5bcfda3a75928f4c2&pid=1-s2.0-S1359028624000585-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}