Materials Genome Engineering Advances最新文献

{"title":"How to work together for engineering materials!","authors":"Shuichi Iwata","doi":"10.1002/mgea.18","DOIUrl":"10.1002/mgea.18","url":null,"abstract":"<p>To share and utilize data effectively for collaborative work, a common understanding of the knowledge behind the data, including its context and meaning, is a fundamental requirement. This paper focuses on the gaps that hinder common understanding between the real world and the data space, acting as barriers between systems, organizations, data spaces, and disciplines. To understand the core reasons and devise strategies for bridging the gap, the author has endeavored to synthesize a case study of material data activities from two perspectives: diachronic and synchronic, which is framed into a two-step process, involving the establishment of intersubjectivity among experts and interobjectivity among materials/substances data. As a result, the author has formulated an action plan for the digitization of engineering materials, encompassing tacit knowledge, know-how, and intellectual property rights to establish a foundation for their use with traceability, interoperability, and reusability. In order to create a conceptual framework that enhances a productive ecosystem facilitated by networked materials and substance databases, this plan is conclusively based on two key steps: fostering interactions among experts rooted in substances and materials and standardizing digitized data related to substances/materials based on their geospatial structural information.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.18","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138966441","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":"Prospects of materials genome engineering frontiers","authors":"Jianxin Xie","doi":"10.1002/mgea.17","DOIUrl":"10.1002/mgea.17","url":null,"abstract":"<p>Materials genome engineering represents the new frontier of materials research, and is disrupting the conventional “trial and error” paradigm for materials innovation. In the present perspective, the author reflects on the major achievements already made in five sub-domains, including high-efficiency materials computation and design, revolutionary experimental technologies, materials big data technologies, research and development of advanced materials, and industrial applications. Furthermore, the author lays out five crucial directions of future efforts for maturing the relevant technologies. These directions include cross-scale modeling and computational design, artificial intelligence for materials science, automatic and intelligent experimentation, digital twin, and data resource management and sharing.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.17","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139009069","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":"High-throughput experimental techniques for corrosion research: A review","authors":"Chenhao Ren,&nbsp;Lingwei Ma,&nbsp;Dawei Zhang,&nbsp;Xiaogang Li,&nbsp;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,&nbsp;Xiaobing Huang,&nbsp;Wei Cai,&nbsp;Tianlin Huang,&nbsp;Guilin Wu,&nbsp;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 × 10⁶ nm² 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,&nbsp;Yong Xu,&nbsp;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,&nbsp;Turab Lookman,&nbsp;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,&nbsp;Dongdong Lu,&nbsp;Jinwei Cao,&nbsp;Fu Zhao,&nbsp;Guang Feng,&nbsp;Chen Xu,&nbsp;Yonghong Deng,&nbsp;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}

{"title":"Toward learning steelmaking—A review on machine learning for basic oxygen furnace process","authors":"Maryam Khaksar Ghalati,&nbsp;Jianbo Zhang,&nbsp;G. M. A. M. El-Fallah,&nbsp;Bogdan Nenchev,&nbsp;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>¹</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>²</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>^{3, 4}</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>₁ + <i>x</i>₂ + … + <i>x</i>_<i>n</i> = 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}