Materials Genome Engineering Advances最新文献

{"title":"Advances in data-assisted high-throughput computations for material design","authors":"Dingguo Xu,&nbsp;Qiao Zhang,&nbsp;Xiangyu Huo,&nbsp;Yitong Wang,&nbsp;Mingli Yang","doi":"10.1002/mgea.11","DOIUrl":"https://doi.org/10.1002/mgea.11","url":null,"abstract":"<p>Extensive trial and error in the variable space is the main cause of low efficiency and high cost in material development. The experimental tasks can be reduced significantly in the case that the variable space is narrowed down by reliable computer simulations. Because of their numerous variables in material design, however, the variable space is still too large to be accessed thoroughly even with a computational approach. High-throughput computations (HTC) make it possible to complete a material screening in a large space by replacing the conventionally manual and sequential operations with automatic, robust, and concurrent streamlines. The efficiency of HTC, which is one of the pillars of materials genome engineering, has been verified in many studies, but its applications are still limited by demanding computational costs. Introduction of data mining and artificial intelligence into HTC has become an effective approach to solve the problem. In the past years, many studies have focused on the development and application of HTC and data combined approaches, which is considered as a new paradigm in computational materials science. This review focuses on the main advances in the field of data-assisted HTC for material research and development and provides our outlook on its future development.</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.11","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50134497","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 construction for the deformation mechanism diagram and dynamic recrystallization of a bimodal-sized particle-reinforced Ti-2.5Zr-2Al-1(Si,C) titanium alloy","authors":"Yixin An,&nbsp;Kechao Zhou,&nbsp;Shaohong Wei,&nbsp;Shiyan Zhu,&nbsp;Yechen Deng,&nbsp;Yangzhihong Xiao,&nbsp;Xiaoyong Zhang,&nbsp;Yubin Ke,&nbsp;Bingfeng Wang","doi":"10.1002/mgea.3","DOIUrl":"https://doi.org/10.1002/mgea.3","url":null,"abstract":"<p>An in situ autogenous particle-reinforced Ti-2.5Zr-2Al-1(Si,C) titanium alloy is prepared by vacuum induction melting. The wide range of an effective strain between 0.2 and 1.2 and the corresponding microstructure are obtained by the double-cone high-throughput compression test and finite element simulation. The deformation mechanism diagram with strains of 0.2–1.2 and strain rates of 0.7–1.5 s⁻¹ at 900°C is constructed. When the strain rate is 1.3 s⁻¹, dynamic recovery occurs in the small strain range (&lt;0.377), dynamic recrystallization (DRX) occurs in the medium strain range (0.377–1.182), and deformation instability occurs in the large strain range (&gt;1.182), resulting in the deformation bands. High-angle annular dark field and high-resolution transmission electron microscopy are used to determine the existence of bimodal particle distribution, namely micron-scale TiC particles and nano-scale Ti₅Si₃ and (Zr, Si) particles. The average radius of the (Zr, Si) nanoparticles measured by small angle neutron scattering is 19.3 nm, and the volume fraction is 0.35%. DRX grains with an average size of 0.49 μm are obtained at 900°C, strain rate of 1.3 s⁻¹, and strain of about 0.6. Micron-scale particles stimulated DRX nucleation, while nanoscale particles hindered the growth of new grains, resulting in grain refinement.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50150561","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":"Ordering phenomena in ternary transition-metal dichalcogenides: Critical role of lattice symmetry and vdW interaction","authors":"Tianxu Zhang,&nbsp;Linggang Zhu,&nbsp;Hanyu Liu,&nbsp;Jian Zhou,&nbsp;Zhimei Sun","doi":"10.1002/mgea.7","DOIUrl":"10.1002/mgea.7","url":null,"abstract":"<p>Concentrated solid solution materials with huge compositional design space and normally unexpected property attract extensive interests of researchers. In these emerging materials, local composition fluctuation such as short-range order (SRO), has been observed and found to have nontrivial effects on material properties, and thus can be utilized as an additional degree of freedom for material optimization. To exploit SRO, its interplay with factors beyond element-level property, including lattice symmetry and bonding environment, should be clarified. In this work by using layered transition-metal dichalcogenide Mo(X_0.5X′_0.5)₂ (X/X′ = O, S, Se, or Te) with mixed element in the non-metal sublattice as the platform, the ordering phenomena are systematically studied using multiscale simulations. As expected, electronegativity difference between X and X′ strongly regulates SRO. Additionally, SRO and long-range order (LRO) are observed in the 2H and T/T′ phase of MoXX′, respectively, indicating a strong influence of lattice symmetry on SRO. More importantly, as vdW interaction is introduced, the SRO structure in 2H-MoXX′ bilayer can be re-configured, while the LRO in T/T′-MoXX′ remains unchanged. Electronic insights for SRO and the resultant property variation are obtained. This work presents a thorough understanding of SRO in bonding complex systems, benefiting the SRO-guided material designs.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135741565","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":"Materials Genome Engineering Advances: A new journal dedicated to digital and intelligent materials research and development","authors":"Jianxin Xie","doi":"10.1002/mgea.9","DOIUrl":"https://doi.org/10.1002/mgea.9","url":null,"abstract":"<p>On behalf of the editorial team, I am delighted to announce the inauguration of the journal, <i>Materials Genome Engineering Advances</i> co-published by Wiley, University of Science and Technology Beijing and Chinese Materials Research Society. <i>Materials Genome Engineering Advances</i> is a pioneering journal dedicated to the emergent research field of materials genome engineering (MGE). It encompasses all three core technologies (high-efficiency computation, advanced experimentation, and big data technology) and focuses on their seamless integration within the materials science landscape.</p><p>Materials research and development (R&amp;D) form the foundational core for advanced manufacturing and act as a precursor to high technology. Traditionally, however, the discovery, development, and deployment of materials have depended on trial-and-error methodologies and heavily influenced by human intuition and experience. These approaches, while sometimes effective, are often costly, labor-intensive, and time-consuming, which has notably hindered the pace of progress within materials R&amp;D. The advent of the latest scientific and technological advances and industrial revolution demands a substantial transformation in the methodologies used in materials R&amp;D, shifting toward more efficient and innovative modes of operation.</p><p>To address this grand challenge, materials scientists have introduced the concept of <i>materials genome</i> by drawing an analogy with <i>human genome</i>. MGE employs high-efficiency computational methods, advanced experimental techniques, as well as database and big data technology. These methodologies are designed to deepen the understanding and accelerate the establishment of the complex relationships between materials composition, microstructure, processing, properties, and performance. The overarching goal of MGE is to transcend the traditional trial-and-error approaches, fostering the development of new theories, methods, and paradigms. This transformation has the potential to fundamentally enhance the efficiency and cost-effectiveness of materials R&amp;D, thereby accelerating the iterative development of new materials and setting a new standard for the field.</p><p>Through concerted and coordinated efforts worldwide, MGE has emerged as one of the most critical and pioneering research fields in materials science and engineering. What makes MGE particularly significant is its interdisciplinary nature. It has garnered interest not only from materials scientists but also from researchers specializing in computer science, data science, electrical engineering, and other related fields. This diverse appeal has catalyzed the rapid expansion of the MGE research community, resulting in a substantial increase in the number of publications on this subject. However, these publications are currently dispersed across traditional experimental materials research journals and a few computational research journals. There has","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50115673","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":"Atomistic simulations of nucleation and growth of CaCO3 with the influence of inhibitors: A review","authors":"Yue Li,&nbsp;Hongbo Zeng,&nbsp;Hao Zhang","doi":"10.1002/mgea.4","DOIUrl":"https://doi.org/10.1002/mgea.4","url":null,"abstract":"Calcium carbonate (CaCO3) is a crucial mineral with great scientific relevance in biomineralization and geoscience. However, excessive precipitation of CaCO3 is posing a threat to industrial production and the aquatic environment. The utilization of chemical inhibitors is typically considered an economical and successful route for addressing the scaling issues, while the underlying mechanism is still debated and needs to be further investigated. In this context, a deep understanding of the crystallization process of CaCO3 and how the inhibitors interact with CaCO3 nuclei and crystals are of great significance in evaluating the performance of scale inhibitors. In recent years, with the rapid development of computing facilities, computer simulations have provided an atomic‐level perspective on the kinetics and thermodynamics of possible association events in CaCO3 solutions as well as the predictions of nucleation pathway and growth mechanism of CaCO3 crystals as a complement to experiment. This review surveys several computational methods and their achievements in this field with a focus on analyzing the functional mechanisms of different types of inhibitors. A general discussion of the current challenges and future directions in applying atomistic simulations to the discovery, design, and development of more effective water‐scale inhibitors is also discussed.","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50125412","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":"On the application of high-throughput experimentation and data-driven approaches in metallic glasses","authors":"Weijie Xie,&nbsp;Weihua Wang,&nbsp;Yanhui Liu","doi":"10.1002/mgea.8","DOIUrl":"https://doi.org/10.1002/mgea.8","url":null,"abstract":"<p>Materials genome engineering (MGE) has been successfully applied in various fields, resulting in a series of novel materials with excellent performance. Significant progress has been made in high-throughput simulation, experimentation, and data-driven techniques, enabling the effective prediction, rapid synthesis, and characterization of many classes of materials. In this brief review, we introduce the achievements made in the field of metallic glasses (MGs) using MGE, in particular high-throughput experimentation and data-driven approaches. High-throughput experiments help to efficiently synthesize and characterize many materials in a short period of time, enabling the construction of high-quality material databases for data-driven methods. Paired with machine learning, potential alloys of desired properties may be revealed and predicted. Along with the progress in computational power and algorithms of machine learning, the complex composition-structure-properties relationship is hopefully established, which in turn help efficient and precise prediction of new MGs.</p>","PeriodicalId":100889,"journal":{"name":"Materials Genome Engineering Advances","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mgea.8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50125414","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}