Journal of Computational Intelligence in Materials Science最新文献

Energy Utilization and Conversion in Modern Biomass Conversion Technologies 现代生物质转换技术中的能量利用和转换

Journal of Computational Intelligence in Materials Science Pub Date : 2024-01-06 DOI: 10.53759/832x/jcims202402001

Nancy Jan Sliper

{"title":"Energy Utilization and Conversion in Modern Biomass Conversion Technologies","authors":"Nancy Jan Sliper","doi":"10.53759/832x/jcims202402001","DOIUrl":"https://doi.org/10.53759/832x/jcims202402001","url":null,"abstract":"This paper provides a review on the current state of biomass conversion technologies that are in use and those that could play a significant role in the future, such as those that might be linked to carbon dioxide (CO2) collection and sequestered technology. Since the transportation industry is poised to become the most important new market for large-scale efficient biomass usage, here is where most of the focus will be placed. Bio-energy contribution, now estimated at 40EJ to 55 EJ per year, is expected to expand significantly in the future. Nevertheless, the precise objective of bio-energy will be dependent on the competitiveness aspect with bio-fuels and on agriculture policy globally. For the rest of this century as least, observations suggest a range of 200–300 EJ, rendering biomass a more significant alternatives of energy supply compared to mineral oil. The need to update bio-energy practices so they are compatible with sustainable development strategies is a major concern. It is expected that within the next two to three decades, the cost of electricity generated using sophisticated conversion concepts (such as gasification and contemporary co-firing and gasification) and contemporary biomass sourced fuels (e.g., hydrogen, methanol, and ethyl alcohol from the lignocellulosic biomass) will be competitive with conventional energy sources (partly based on price development with petroleum). An even more efficient and cost-effective biofuel production system may be developed from sugarcane-centric ethanol within the tropical climates.","PeriodicalId":240206,"journal":{"name":"Journal of Computational Intelligence in Materials Science","volume":"109 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140513151","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}

引用次数: 0

A Review on Background and Applications of Machine Learning in Materials Research 机器学习在材料研究中的背景与应用综述

Journal of Computational Intelligence in Materials Science Pub Date : 2023-06-30 DOI: 10.53759/832x/jcims202301008

Robert Ahmed, Christna Ahler

{"title":"A Review on Background and Applications of Machine Learning in Materials Research","authors":"Robert Ahmed, Christna Ahler","doi":"10.53759/832x/jcims202301008","DOIUrl":"https://doi.org/10.53759/832x/jcims202301008","url":null,"abstract":"In recent decades, Artificial Intelligence (AI) has garnered considerable interest owing to its potential to facilitate greater levels of automation and speed up overall output. There has been a significant increase in the quantity of training data sets, processing capacity, and deep learning techniques that are all favorable to the widespread use of AI in fields like material science. Attempting to learn anything new by trial and error is a slow and ineffective approach. Therefore, AI, and particularly machine learning, may hasten the process by gleaning rules from information and constructing predictive models. In traditional computational chemistry, human scientists give the formulae, and the computer just crunches the numbers. In this article, we take a look back at the ways in which artificial intelligence has been put to use in the creation of new materials, such as in their design, performance prediction, and synthesis. In these programs, an emphasis is placed on the specifics of AI methodology implementation and the benefits gained over more traditional approaches. The last section elaborates, from both an algorithmic and an infrastructural perspective, where AI is headed in the future.","PeriodicalId":240206,"journal":{"name":"Journal of Computational Intelligence in Materials Science","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120962203","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}

引用次数: 0

Machine Learning Approches for Evaluating the Properties of Materials 评估材料性能的机器学习方法

Journal of Computational Intelligence in Materials Science Pub Date : 2023-06-10 DOI: 10.53759/832x/jcims202301007

Nanna Ahlmann Ahm

{"title":"Machine Learning Approches for Evaluating the Properties of Materials","authors":"Nanna Ahlmann Ahm","doi":"10.53759/832x/jcims202301007","DOIUrl":"https://doi.org/10.53759/832x/jcims202301007","url":null,"abstract":"Machine Learning for Materials Science is a primer on the subject that also delves into the specifics of where ML might be\u0000applied to materials science research. With a focus on where to collect data and some of the issues when choosing a\u0000strategy, this article includes example approaches for ML applied to experiments and modeling, such as the first steps in\u0000the procedure for constructing an ML solution for a materials science problem. The lengthy cycles of development,\u0000inefficiencies, and higher costs of conventional techniques of material discovery, such as the density functional theory-\u0000based and empirical trials and errors approach, make it impossible for materials research to keep up with modern\u0000advances. Hence, machine learning is extensively employed in material detection, material design, and material analysis\u0000because of its cheap computing cost and fast development cycle, paired with strong data processing and good prediction\u0000performance. This article summarizes recent applications of ML algorithms within different material science fields,\u0000discussing the advancements that are needed for widespread application, and details the critical operational procedures\u0000involved in evaluating the features of materials using ML.","PeriodicalId":240206,"journal":{"name":"Journal of Computational Intelligence in Materials Science","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116073629","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}

引用次数: 0

Automated Design Using Machine Learning in Materials Engineering - An Explicit Forecasts 在材料工程中使用机器学习的自动化设计-一个明确的预测

Journal of Computational Intelligence in Materials Science Pub Date : 2023-05-06 DOI: 10.53759/832x/jcims202301006

Birgir Guomundsson, Gunnar Lorna

{"title":"Automated Design Using Machine Learning in Materials Engineering - An Explicit Forecasts","authors":"Birgir Guomundsson, Gunnar Lorna","doi":"10.53759/832x/jcims202301006","DOIUrl":"https://doi.org/10.53759/832x/jcims202301006","url":null,"abstract":"Every discipline of physics, including materials science, has been profoundly influenced by the arrival of\u0000algorithmic breakthroughs in the domain of machine learning. Many important advances have been made by combining\u0000materials data (computed and measured) with different machine learning approaches to solve difficult problems like,\u0000creating effectual and extrapolative surrogate prototypes for a wide variety of material parameters, down-selecting and\u0000screening novel candidate materials for particular application, and structuring novel approaches to accelerate and enhance\u0000atomistic and molecular simulations. Although current implementations have shown some of the promise of data-enabled\u0000pathways, it has become evident that success in this area will depend on our capacity to interpret, explain, and justify the\u0000results of a machine learning approach on the basis of our professional knowledge in the field. This article reviews the\u0000most important machine learning applications in materials engineering. In addition, we present a short overview of a\u0000number of methods that have proven useful in deriving physically relevant insights, design-centric knowledge, and causal\u0000links from materials engineering. Last but not least, we highlight some of the next prospects and obstacles that the\u0000materials community will encounter in this dynamic and fast developing industry.","PeriodicalId":240206,"journal":{"name":"Journal of Computational Intelligence in Materials Science","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130788418","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}

引用次数: 0

Nano-Antibacterial Materials as an Alternative Antimicrobial Strategy 纳米抗菌材料作为一种替代的抗菌策略

Journal of Computational Intelligence in Materials Science Pub Date : 2023-04-28 DOI: 10.53759/832x/jcims202301005

A. H

{"title":"Nano-Antibacterial Materials as an Alternative Antimicrobial Strategy","authors":"A. H","doi":"10.53759/832x/jcims202301005","DOIUrl":"https://doi.org/10.53759/832x/jcims202301005","url":null,"abstract":"Bacterial infections continue to be a leading cause of death and disability worldwide. Considering the increment in bacteria resistant to antibiotic and the prevalence of illnesses linked to biofilms, it is imperative that new strategies of killing bacteria be developed. As a result, recent years have seen a surge in interest in nanoparticle-based materials for use in antimicrobial chemotherapy. Bacterial infections have remained a significant source of death and morbidity, despite the availability of many powerful antibiotics and other antimicrobial measures. Because of rising worries about drug-resistant bacteria and diseases linked to biofilms, there is an urgent need to create new bactericidal techniques. As a result, the science of antimicrobial chemotherapeutic has focused heavily on recently developed nanoparticle-based materials. Nanoparticles are discussed in this article in terms of their antimicrobial properties, their method of action, their influence on drug-resistant microorganisms, and the hazards associated with their usage. Nanoparticles' special characteristics and their mode of action as antimicrobial properties are examined in depth, as are the factors that contribute to their performance in a clinical environment.","PeriodicalId":240206,"journal":{"name":"Journal of Computational Intelligence in Materials Science","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134300939","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}

引用次数: 0

Designing Material Structures and Standards Considering Customer Requirements 根据客户要求设计材料结构和标准

Journal of Computational Intelligence in Materials Science Pub Date : 2023-04-15 DOI: 10.53759/832x/jcims202301004

Jiaxiang Xue, Zhixin Liu

引用次数: 1

A Survey of Factors and Life Cycle Assessment in Selection of Green Construction Materials 绿色建筑材料选择的因素与生命周期评价研究

Journal of Computational Intelligence in Materials Science Pub Date : 2023-04-08 DOI: 10.53759/832x/jcims202301003

Zheng Huijiang Wang

{"title":"A Survey of Factors and Life Cycle Assessment in Selection of Green Construction Materials","authors":"Zheng Huijiang Wang","doi":"10.53759/832x/jcims202301003","DOIUrl":"https://doi.org/10.53759/832x/jcims202301003","url":null,"abstract":"During both the preliminary design phase, when broad, overarching decisions about the building's function and appearance are made, and the detailed construction plan level, material selection plays a crucial part in realizing the 'Green Buildings' goal (when materials present on the market are chosen). Architects and engineers responsible for making this option typically lack access to assessment tools aimed at assisting them in the selection of materials, despite the fact that this second factor is just as crucial to the actual fulfillment of 'greenness' standards. The environment is being harmed by human activities such as manufacturing, transportation, and mining. Saving the planet's natural resources has proven difficult for scientists and engineers since doing so means lowering society's performance, development pace, and standard of living. We have gone a long way in creating tools that might prevent more ecological damage and slow the depletion of vital resources. The notion of \"green buildings\" is based on the same idea. Increasing a building's energy efficiency utilizing green natural or renewable resources rather than non-renewable resources is a key component of green construction, according to this perspective. In this study, we discuss the criteria that should be used to pick green building materials.","PeriodicalId":240206,"journal":{"name":"Journal of Computational Intelligence in Materials Science","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126699139","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}

引用次数: 1

A Critical Review of Nanoparticles and Nano Catalyst 纳米颗粒和纳米催化剂的研究进展

Journal of Computational Intelligence in Materials Science Pub Date : 2023-01-09 DOI: 10.53759/832x/jcims202301002

Tian Chen, Davin Meng

{"title":"A Critical Review of Nanoparticles and Nano Catalyst","authors":"Tian Chen, Davin Meng","doi":"10.53759/832x/jcims202301002","DOIUrl":"https://doi.org/10.53759/832x/jcims202301002","url":null,"abstract":"Catalysis holds a significant position in the field of chemistry, wherein it manifests in three distinct directions that exhibit minimal overlap: heterogeneous, enzymatic, and homogeneous. Heterogeneous and homogeneous catalysis are recognized as distinct fields championed by two scientific societies, namely solid state and molecular chemistry. Despite their differences, both domains share a common goal of seeking to enhance catalytic performance. Nanocatalysis has gained prominence as a burgeoning scientific discipline in recent times, owing to its exceptional levels of activity, selectivity, and productivity. The distinctive characteristics of nanocatalysts arise from their nanoscale dimensions, morphology, and significantly elevated surface area to volume ratio. These structural and electronic modifications distinguish them from their bulk counterparts, resulting in unique properties. At the nanoscale level, the principles of quantum chemistry and classical physics are not applicable. In materials characterised by robust chemical bonding, the degree of electron delocalization can be substantial and may exhibit size-dependent variability. The primary objective of this review is to expound upon the critical understanding of nanocatalysis, detailing how the different catalytic feature and other particle features of nanomaterials are contingent on their structure and size at an atomic level.","PeriodicalId":240206,"journal":{"name":"Journal of Computational Intelligence in Materials Science","volume":"185 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131811797","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}

引用次数: 1

A Survey on Multi Agent System and Its Applications in Power System Engineering 多智能体系统及其在电力系统工程中的应用综述

Journal of Computational Intelligence in Materials Science Pub Date : 2023-01-05 DOI: 10.53759/832x/jcims202301001

Madeleine Wang Yue Dong

{"title":"A Survey on Multi Agent System and Its Applications in Power System Engineering","authors":"Madeleine Wang Yue Dong","doi":"10.53759/832x/jcims202301001","DOIUrl":"https://doi.org/10.53759/832x/jcims202301001","url":null,"abstract":"An Intelligent Agent (IA) is a type of autonomous entity in the field of Artificial Intelligence (AI) that gathers information about its surroundings using sensors, takes action in response to that information using actuators (\"agent\" part), and guides its behavior to achieve predetermined results (i.e. it is rational). Agents that are both intelligent and able to learn or utilize information to accomplish their tasks would be ideal. Similar to how economists study agents, cognitive scientists, ethicists, philosophers of practical reason and researchers in a wide range of other disciplines study variations of the IAmodel used in multidisciplinary socio-cognitive modelling and computer social simulation models. In this article, the term \"Multi-Agent System\" (MAS) has been used to refer to a system in which two or more autonomous entities communicate with one another. The key objective of this research is to provide a critical analysis of MAS and its applications in power systems. A case study to define the application of MAS in power system is also provided, using a critical implementation of fuzzy logic controllers.","PeriodicalId":240206,"journal":{"name":"Journal of Computational Intelligence in Materials Science","volume":"45 4-5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133005681","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}

引用次数: 2