{"title":"High-throughput experimental techniques for corrosion research: A review","authors":"Chenhao Ren, Lingwei Ma, Dawei Zhang, Xiaogang Li, Arjan Mol","doi":"10.1002/mgea.20","DOIUrl":"10.1002/mgea.20","url":null,"abstract":"<p>High-throughput experimental techniques can accelerate and economize corrosion evaluation, and thus, have great potential in the development of new materials for corrosion protection such as corrosion-resistant metals, corrosion inhibitors, and anticorrosion coatings. This concise review highlights high-throughput experimental techniques that have been recently applied for corrosion research, including (i) the high-throughput preparation of metal samples in the form of thin films or bulk materials, (ii) high-throughput experiments based on corrosive solutions with independent or gradient parameters, (iii) high-throughput evaluation of changes in physicochemical properties, and (iv) high-throughput corrosion evaluation by electrochemical methods. To advance automated and intelligent corrosion research, future directions for the development of the high-throughput corrosion experimental and characterization techniques are also discussed.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.20","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138982957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"3D characterization of abnormal grain growth in nanocrystalline nickel","authors":"Wanquan Zhu, Xiaobing Huang, Wei Cai, Tianlin Huang, Guilin Wu, Xiaoxu Huang","doi":"10.1002/mgea.19","DOIUrl":"10.1002/mgea.19","url":null,"abstract":"<p>Abnormal grain growth, a pervasive phenomenon witnessed during the annealing of nanocrystalline metals, precipitates a swift diminution of the distinctive properties inherent to such materials. Historically, conventional transmission electron microscopy has struggled to efficiently procure comprehensive five-parameter crystallographic information from a substantial number of grain boundaries in nanocrystalline metals, thus inhibiting a deeper understanding of abnormal grain growth behavior within nanocrystalline materials. In this study, we utilize a high-throughput characterization method—three-dimensional orientation mapping in the TEM (3D-OMiTEM) to characterize the crystallographic five-parameter character of grain boundaries with an area of over 3.4 × 10<sup>6</sup> nm<sup>2</sup> in an abnormally grown nanocrystalline nickel sample. When coupled with existing theoretical simulation results, it is discerned that the grain boundary population shows a relatively large scatter when it is correlated to the calculated grain boundary energy; the grain boundaries of abnormally grown grains exhibit lower grain boundary energy compared to those that have not undergone abnormal growth. Merging high-throughput grain boundary information obtained from three-dimensional orientation mapping data with grain boundary properties derived from high-throughput theoretical calculations following the concept of materials genome engineering will undoubtedly facilitate further advancements in comprehending and discerning the interfacial behaviors of crystalline materials.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.19","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138590066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Ab initio artificial intelligence: Future research of Materials Genome Initiative","authors":"He Li, Yong Xu, Wenhui Duan","doi":"10.1002/mgea.16","DOIUrl":"10.1002/mgea.16","url":null,"abstract":"<p>The marriage of artificial intelligence (AI) and Materials Genome Initiative (MGI) could profoundly change the landscape of modern materials research, leading to a new paradigm of data-driven and AI-driven materials discovery. In this perspective, we will give an overview on the central role of AI in the MGI research. In particular, an emerging research field of ab initio AI, which applies state-of-the-art AI techniques to help solve bottleneck problems of ab initio computation, will be introduced. The development of ab initio AI will greatly accelerate high-throughput computation, promote the construction of large materials database, and open new opportunities for future research of MGI.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.16","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139246799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Multi-objective optimization and its application in materials science","authors":"Bofeng Shi, Turab Lookman, Dezhen Xue","doi":"10.1002/mgea.14","DOIUrl":"10.1002/mgea.14","url":null,"abstract":"<p>Optimizing more than one property is inevitable in designing new materials; however, some properties are usually improved at the expense of others. Multi-objective optimization methods in engineering and computer science have proven to be an effective means to optimize several different properties simultaneously. Here, we reviewed these approaches including scalarization, evolutionary algorithms, and especially Bayesian optimization. Their promising applications to a number of materials problems are also discussed in the paper.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.14","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136351430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Building materials genome from ground-state configuration to engineering advance","authors":"Zi-Kui Liu","doi":"10.1002/mgea.15","DOIUrl":"10.1002/mgea.15","url":null,"abstract":"<p>Individual phases are commonly considered as the building blocks of materials. However, the accurate theoretical prediction of properties of individual phases remains elusive. The top-down approach by decoding genomic building blocks of individual phases from experimental observations is nonunique. The density functional theory (DFT), as a state-of-the-art solution of quantum mechanics, prescribes the existence of a ground-state configuration at 0 K for a given system. It is self-evident that the ground-state configuration alone is insufficient to describe a phase at finite temperatures as symmetry-breaking non-ground-state configurations are excited statistically at temperatures above 0 K. Our multiscale entropy approach (recently terms as Zentropy theory) postulates that the entropy of a phase is composed of the sum of the entropy of each configuration weighted by its probability plus the configurational entropy among all configurations. Consequently, the partition function of each configuration in statistical mechanics needs to be evaluated by its free energy rather than total energy. The combination of the ground-state and symmetry-breaking non-ground-state configurations represents the building blocks of materials and can be used to quantitatively predict free energy of individual phases with the free energy of each configuration predicted from DFT as well as all properties derived from free energy of individual phases.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.15","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135138228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Development of high-throughput wet-chemical synthesis techniques for material research","authors":"Zhuyang Chen, Dongdong Lu, Jinwei Cao, Fu Zhao, Guang Feng, Chen Xu, Yonghong Deng, X.-D. Xiang","doi":"10.1002/mgea.5","DOIUrl":"https://doi.org/10.1002/mgea.5","url":null,"abstract":"<p>Combining material big data with artificial intelligence constitutes the fourth paradigm of material research. However, the sluggish development of high-throughput (HT) experimentation has resulted in a lack of experimentally verified and validated material data, which has become the bottleneck of data-driven material research. Wet-chemical synthesis has the benefits of low equipment cost and scalability, but traditional wet-chemical techniques are time-consuming and ineffective at disclosing the interrelationships between synthesis, compositions, structures, and performance. Constructing a HT workflow in wet-chemical synthesis is crucial to achieving the preparation of multidimensional materials and establishing the composition–structure–synthesis–performance relationships of functional materials for diverse applications. In this review, the most recent development in HT wet-chemical synthesis techniques for material research are analyzed in depth. Additionally, the application of HT wet-chemical synthesis in the fabrication of advanced hydrogels and catalysts is demonstrated through illustrative instances. Finally, this review suggests possible paths for enhancing the efficiency of HT experimentation and data acquisition in order to facilitate more effective material discovery.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50140600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maryam Khaksar Ghalati, Jianbo Zhang, G. M. A. M. El-Fallah, Bogdan Nenchev, Hongbiao Dong
{"title":"Toward learning steelmaking—A review on machine learning for basic oxygen furnace process","authors":"Maryam Khaksar Ghalati, Jianbo Zhang, G. M. A. M. El-Fallah, Bogdan Nenchev, Hongbiao Dong","doi":"10.1002/mgea.6","DOIUrl":"https://doi.org/10.1002/mgea.6","url":null,"abstract":"<p>Basic oxygen furnace (BOF) steelmaking is the most widely used process in global steel production today, accounting for around 70% of the industry's output. Due to the physical, mechanical, and chemical complexities involved in the process, conventional monitoring and control methods are often pushed to their limits. The increasing global competition has created a demand for new methods to monitor and control the BOF steelmaking process. Over the past decade, Machine Learning (ML) techniques have garnered substantial attention, offering a promising pathway to enhance efficiency and suitability of steel production. This paper presents the first comprehensive review of ML applications in the BOF steelmaking process. We provide an introduction to both fields: an overview of the BOF steelmaking process and ML. We analyze the existing work on ML applications in BOF steelmaking and synthesize common concepts into categories, supporting the identification of common use cases and approaches. This analysis concludes with the elaboration of challenges, potential solutions, and a future outlook for further research directions.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50152891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Opinion: Chemical potential and materials genome","authors":"Long-Qing Chen","doi":"10.1002/mgea.13","DOIUrl":"10.1002/mgea.13","url":null,"abstract":"<p>The Materials Genome Initiative started almost exactly a dozen years ago in the US.<span><sup>1</sup></span> However, if one asks the question “what is materials genome?” to 100 people, it is a good bet that one would get 100 different answers. To the author's knowledge, there is still no generally agreeable definition of what materials genome is, unlike from the human genome, which has a much clearer definition.<span><sup>2</sup></span> In some way, the Materials Genome Initiative has been mainly used as a rallying slogan for advocating multiscale modeling, closed-loop integration, and iteration between computation and experiments, and more recently, the applications of data science, machine learning, and artificial intelligence to materials science and engineering.<span><sup>3, 4</sup></span> Therefore, the main purpose of this article is to offer the author's perspective on what could be considered as the materials genome.</p><p>Gibbs defined a simple system as one without interfacial, gravitational, electrical, and magnetic contributions and introduced a set of basic thermodynamic variables to describe an equilibrium state of a simple system.</p><p>An equilibrium state is defined as a state in which all the state variables no longer vary with time. However, it should be noted that an equilibrium state does not have to be a stable state; it can be stable, metastable, or unstable. An unstable equilibrium state is intrinsically unstable with respect to any small fluctuations in the state variables, whereas a metastable equilibrium state is stable against small fluctuations in state variables but unstable with respect to large fluctuations. A stable equilibrium state is stable against any fluctuations, large or small. Therefore, unstable and metastable equilibrium states can only be arrested kinetically in practice. However, we hypothesize that all states, including unstable and metastable states, can be described by the same set of basic state variables and the fundamental equation of thermodynamics.</p><p>These independent variables are called the natural variables for <i>U</i> because only when <i>U</i> is expressed as a function of its <i>n</i> + 2 natural variables, Equation (1) is a fundamental equation of thermodynamics.</p><p>Therefore, we have 2<i>n</i> + 5 total basic thermodynamic variables, which are related by <i>n</i> + 3 equations with <i>n</i> + 2 of them being equations of state (Equation 4) and one integrated fundamental equation of thermodynamics (Equation 1). Therefore, the number of independent variables, given by 2<i>n</i> + 5 − (<i>n</i> + 3) = <i>n</i> + 2, is often called the number of degrees of freedom for a system.</p><p>Here, it should be noted that <i>x</i><sub>1</sub> + <i>x</i><sub>2</sub> + … + <i>x</i><sub><i>n</i></sub> = 1; there are only <i>n</i> − 1 independent composition variables. All the thermodynamic properties of a material can be determined from the knowledge of chemical potential <i>μ</i> as a func","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.13","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135106829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Data-driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology","authors":"Xiaoxiao Geng, Feiyang Wang, Hong-Hui Wu, Shuize Wang, Guilin Wu, Junheng Gao, Haitao Zhao, Chaolei Zhang, Xinping Mao","doi":"10.1002/mgea.10","DOIUrl":"https://doi.org/10.1002/mgea.10","url":null,"abstract":"<p>With the development of new information technology, big data technology and artificial intelligence (AI) have accelerated material research and development and industrial manufacturing, which have become the key technology driving a new wave of global technological revolution and industrial transformation. This review introduces the data resources and databases related to steel materials. It then examines the fundamental strategies and applications of machine learning (ML) in the design and discovery of steel materials, including ML models based on experimental data, industrial manufacturing data, and simulation data, respectively. Given the advancements in big data, AI/ML, and new communication technologies, an intelligent manufacturing mode featuring digital twins is deemed critical in guiding the next industrial revolution. Consequently, the application of intelligence manufacturing with digital twins in the iron and steel industry is reviewed and discussed. Furthermore, the applications of ML in service performance prediction of steel products are addressed. Finally, the future development trends for data-driven and AI approaches throughout the entire life cycle of steel materials are prospected. Overall, this work presents an in-depth examination of the integration of data-driven and AI technologies in the steel industry, highlighting their potential and future directions.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.10","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50138152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jin Zhou, Yongqing Chen, Yuan Ma, Xiaoxin Zhang, Xing Gong, Yang He, Qingzhi Yan, Lijie Qiao
{"title":"Statistical in situ scanning electron microscopy investigation on the failure of oxide scales","authors":"Jin Zhou, Yongqing Chen, Yuan Ma, Xiaoxin Zhang, Xing Gong, Yang He, Qingzhi Yan, Lijie Qiao","doi":"10.1002/mgea.12","DOIUrl":"https://doi.org/10.1002/mgea.12","url":null,"abstract":"<p>Oxide scales play a pivotal role in obstructing surface chemical and electrochemical reactions, hence hindering chemo-mechanical effects such as liquid metal embrittlement of steels. Therefore, the critical conditions and failure mechanism of the oxide film are of major interest in the safe service of steels. Though in situ microscopic methods may directly visualize the failure mechanism, they are often challenged by the lack of statistically reliable evaluation of the critical conditions. Here, by combining in situ scanning electron microscopy with a tapered specimen tensile test in a single experiment, we uniquely achieve a mechanistic study with statistically reliable quantification of the critical strains for each step of the dynamic process of film rupture. This is demonstrated with the oxide films formed on a ferrite–martensite steel in liquid lead–bismuth eutectic alloy at elevated temperatures, with in situ results falling right into the predictions of the statistical analysis. Explicitly, the integrated experimental methodology may facilitate the materials genome engineering of steels with superior service performance.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.12","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50134495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}