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ABSTRACTThis review systematically analyzes over 200 publications to explore the growing role of data-driven methods and their potential benefits in accelerating alloy development. The review presents a comprehensive overview of different aspects of alloy innovation by machine learning and other computational approaches used in recent years. These methods harness the power of advanced simulation techniques and data analytics to expedite materials’ discovery, predict properties, and optimize performance. Through analysis, significant trends and disparities within the data discerned, while highlighting previously overlooked research gaps, thus underscoring areas that require further exploration. Machine Learning techniques are widely applied across various alloys, with a pronounced emphasis on steel and High Entropy Alloys. Notably, researchers primarily investigate the physical, mechanical, and catalytic properties of materials. In terms of methodology, while 68% of the examined papers rely on a single machine learning model, the remainder employ a range of 2 to 12 models, with Neural Network being the most prevalent choice. However, a notable concern arises as 53% of these papers do not share their dataset, and a staggering 81% do not provide access to their code. Paramount importance of adopting a systematic approach when scrutinizing machine learning methodologies is underscored. Analysis shows lack of consistency and diversity in the methods employed by researchers in the field of alloy development, highlighting the potential for improvement through standardization. The critical analysis of the literature not only reveals prevailing trends and patterns but also shines a light on the inherent limitations within the traditional trial-and-error paradigm.

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification. [ABSTRACT FROM AUTHOR]

Data mining is increasing its popularity in the research of Technology-Enhanced Language Learning and Applied Linguistics in general. It enables a better understanding of progress, performance and possible pitfalls, which would be useful for language learners, teachers and researchers. Until recently it was an unexplored field, but it is expected to grow exponentially in the following years. This article attempts to be a relevant contribution as an instance of empirical research, showing the application of Learning Analytics to the Language MOOC (LMOOC) ‘How to succeed in the English B1 Level Exam.’ The focus or the research was threefold, trying to find out: (1) what types of learning objects students engage with most, (2) what aspects of online interaction relate more strongly to course completion and success, and (3) which are the most prominent student profiles in an LMOOC. Results show that short video-pills are the most powerful learning objects in this type of online courses, the regular submission of automated grading activities is a robust indicator towards course success, and the most prominent student profile in LMOOCs is ‘viewers’, those who access the learning materials but do not submit tasks or engage in online interaction actively, which would explain why the completion rate in LMOOCs is so low. This novel perspective into students’ language learning, which big data has assisted us in, should guide course creators to re-design the LMOOC for the enhancement of the audio-visual content. LMOOC instructors and facilitators should also encourage participants to increase the submission of activities -acknowledging these small achievements through micro-credentialing and badges-, and special attention ought to be paid to the most prominent LMOOC profile, those ‘viewers’ who should be lured into becoming ‘solvers’ or, even better, ‘all-rounders’. [ABSTRACT FROM AUTHOR]

We apply an artificial neural network to model and verify material properties. The neural network algorithm has a unique capability to handle incomplete data sets in both training and predicting, so it can regard properties as inputs allowing it to exploit both composition-property and property-property correlations to enhance the quality of predictions, and can also handle a graphical data as a single entity. The framework is tested with different validation schemes, and then applied to materials case studies of alloys and polymers. The algorithm found twenty errors in a commercial materials database that were confirmed against primary data sources. [ABSTRACT FROM AUTHOR]

Electronic band structure contains a wealth of information on the electronic properties of a solid and is routinely computed. However, the more difficult problem of designing a solid with a desired band structure is an outstanding challenge. In order to address this inverse band structure design problem, we devise an approach using materials database screening with materials attributes based on the constituent elements, nominal electron count, crystal structure, and thermodynamics. Our strategy is tested in the context of thermoelectric materials, for which a targeted band structure containing both flat and dispersive components with respect to crystal momentum is highly desirable. We screen for thermodynamically stable or metastable compounds containing d8 transition metals coordinated by anions in a square planar geometry in order to mimic the properties of recently identified oxide thermoelectrics with such a band structure. In doing so, we identify 157 compounds out of a total of more than half a million candidates. After further screening based on electronic band gap and structural anisotropy, we explicitly compute the band structures for the several of the candidates in order to validate the approach. We successfully find two new oxide systems that achieve the targeted band structure. Electronic transport calculations on these two compounds, Ba2PdO3 and La4PdO7, confirm promising thermoelectric power factor behavior for the compounds. This methodology is easily adapted to other targeted band structures and should be widely applicable to a variety of design problems. [ABSTRACT FROM AUTHOR]

Tight-binding models provide a conceptually transparent and computationally efficient method to represent the electronic properties of materials. With AFLOW π we introduce a framework for high-throughput first principles calculations that automatically generates tight-binding hamiltonians without any additional input. Several additional features are included in AFLOW π with the intent to simplify the self-consistent calculation of Hubbard U corrections, the calculations of phonon dispersions, elastic properties, complex dielectric constants, and electronic transport coefficients. As examples we show how to compute the optical properties of layered nitrides in the AM N 2 family, and the elastic and vibrational properties of binary halides with CsCl and NaCl structure. [ABSTRACT FROM AUTHOR]

One major shortcoming of existing bi-scale material design systems is the lack of support for inverse design: there is no way to directly edit the large-scale appearance and then rapidly solve for the small-scale details that approximate that look. Prior work is either too slow to provide quick feedback, or limited in the types of small-scale details that can be handled. We present a novel computational framework for inverse bi-scale material design. The key idea is to convert the challenging inverse appearance computation into efficient search in two precomputed large libraries: one including a wide range of measured and analytical materials, and the other procedurally generated and height-map-based geometries. We demonstrate a variety of editing operations, including finding visually equivalent details that produce similar large-scale appearance, which can be useful in applications such as physical fabrication of materials. [ABSTRACT FROM AUTHOR]

Providing better availability to materials data has recently gained new momentum. Many successes abound-large numbers of individual materials databases exist, powerful modeling and data analysis approaches have been developed, and Web-based technologies are available. At the same time, challenges remain: one-stop access is lacking, use of multiple databases at the same time is virtually impossible, using shared data is difficult, and understanding data quality is very hard. In this paper, we review the successes and challenges of accessing digital materials data, especially as new initiatives are starting. We also identify insights from previous work that provide guidance to future progress, including adherence to the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles, in achieving this dream. [ABSTRACT FROM AUTHOR]

With the fast global adoption of the Materials Genome Initiative (MGI), scientists and engineers are faced with the need to conduct sophisticated data analytics on large datasets to extract knowledge that can be used in modeling the behavior of materials. This raises a new problem for materials scientists: how to create and foster interoperability and share developed software tools and generated datasets. A microstructure-informed cloud-based platform (MiCloud™) has been developed that addresses this need, enabling users to easily access and insert microstructure informatics into computational tools that predict performance of engineering products by accounting for microstructural dependencies on manufacturing provenance. The platform extracts information from microstructure data by employing algorithms including signal processing, machine learning, pattern recognition, computer vision, predictive analytics, uncertainty quantification, and data visualization. The interoperability capabilities of MiCloud and its various web-based applications are demonstrated in this case study by analyzing Ti6AlV4 microstructure data via automatic identification of various features of interest and quantifying its characteristics that are used in extracting correlations and causations for the associated mechanical behavior (e.g., yield strength, cold-dwell debit, etc.). The data were recorded by two methods: (1) backscattered electron (BSE) imaging for extracting spatial and morphological information about alpha and beta phases and (2) electron backscatter diffraction (EBSD) for extracting spatial, crystallographic, and morphological information about microtextured regions (MTRs) of the alpha phase. Extracting reliable knowledge from generated information requires data analytics of a large amount of multiscale microstructure data which necessitates the development of efficient algorithms (and the associated software tools) for data recording, analysis, and visualization. The interoperability of these tools and superior effectiveness of the cloud computing approach are validated by featuring several examples of its use in alpha/beta titanium alloys and Ni-based superalloys, reflecting the anticipated computational cost and time savings via the use of web-based applications in implementations of microstructure-informed integrated computational materials engineering (ICME). [ABSTRACT FROM AUTHOR]

The validity of material flow analyses (MFAs) depends on the available information base, that is, the quality and quantity of available data. MFA data are cross-disciplinary, can have varying formats and qualities, and originate from heterogeneous sources, such as official statistics, scientific models, or expert estimations. Statistical methods for data evaluation are most often inadequate, because MFA data are typically isolated values rather than extensive data sets. In consideration of the properties of MFA data, a data characterization framework for MFA is presented. It consists of an MFA data terminology, a data characterization matrix, and a procedure for database analysis. The framework facilitates systematic data characterization by cell-level tagging of data with data attributes. Data attributes represent data characteristics and metainformation regarding statistical properties, meaning, origination, and application of the data. The data characterization framework is illustrated in a case study of a national phosphorus budget. This work furthers understanding of the information basis of material flow systems, promotes the transparent documentation and precise communication of MFA input data, and can be the foundation for better data interpretation and comprehensive data quality evaluation. [ABSTRACT FROM AUTHOR]

Conventionally, high-throughput computational materials searches start from an input set of bulk compounds extracted from material databases, but, in contrast, many real functional materials are heavily engineered mixtures of compounds rather than single bulk compounds. We present a framework and open-source code to automatically construct and analyze possible alloys and solid solutions from a set of existing experimental or calculated ordered compounds, without requiring additional metadata except crystal structure. As a demonstration, we apply this framework to all compounds in the Materials Project to create a new, publicly available database of > 600,000 unique "alloy pair" entries that can be used to search for materials with tunable properties. We exemplify this approach by searching for transparent conductors and reveal candidates that might have been excluded in a traditional screening. This work lays a foundation from which materials databases can go beyond stoichiometric compounds and approach a more realistic description of compositionally tunable materials., Competing Interests: The authors declare no competing interests., (© 2023 The Authors.)

Computational science has seen in the last decades a spectacular rise in the scope, breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still performed using the renaissance model of individual artisans gathered in a workshop, under the guidance of an established practitioner. Great benefits could follow instead from adopting concepts and tools coming from computer science to manage, preserve, and share these computational efforts. We illustrate here our paradigm sustaining such vision, based around the four pillars of Automation, Data, Environment, and Sharing. We then discuss its implementation in the open-source AiiDA platform ( http://www.aiida.net ), that has been tuned first to the demands of computational materials science. AiiDA’s design is based on directed acyclic graphs to track the provenance of data and calculations, and ensure preservation and searchability. Remote computational resources are managed transparently, and automation is coupled with data storage to ensure reproducibility. Last, complex sequences of calculations can be encoded into scientific workflows. We believe that AiiDA’s design and its sharing capabilities will encourage the creation of social ecosystems to disseminate codes, data, and scientific workflows. [ABSTRACT FROM AUTHOR]

The article discusses the importance of studying material objects in biodiversity citizen science, including cameras, smartphones, and information systems, and how these objects shape information practices in citizen science. The article also introduces the objectual practice approach, which mentions how knowledge is constructed iteratively through interaction with epistemic objects, and explains how this approach can be used to study biodiversity citizen science information practices.

The field of materials science and engineering is on the cusp of a digital data revolution. After reviewing the nature of data science and Big Data, we discuss the features of materials data that distinguish them from data in other fields. We introduce the concept of process-structure-property (PSP) linkages and illustrate how the determination of PSPs is one of the main objectives of materials data science. Then we review a selection of materials databases, as well as important aspects of materials data management, such as storage hardware, archiving strategies, and data access strategies. We introduce the emerging field of materials data analytics, which focuses on data-driven approaches to extract and curate materials knowledge from available data sets. The critical need for materials e-collaboration platforms is highlighted, and we conclude the article with a number of suggestions regarding the near-term future of the materials data science field. [ABSTRACT FROM AUTHOR]

The continued advancement of science depends on shared and reproducible data. In the field of computational materials science and rational materials design this entails the construction of large open databases of materials properties. To this end, an A pplication P rogram I nterface (API) following REST principles is introduced for the AFLOWLIB.org materials data repositories consortium. AUIDs ( A flowlib U nique ID entifier) and AURLs ( A flowlib U niform R esource L ocator) are assigned to the database resources according to a well-defined protocol described herein, which enables the client to access, through appropriate queries, the desired data for post-processing. This introduces a new level of openness into the AFLOWLIB repository, allowing the community to construct high-level work-flows and tools exploiting its rich data set of calculated structural, thermodynamic, and electronic properties. Furthermore, federating these tools will open the door to collaborative investigations of unprecedented scope that will dramatically accelerate the advancement of computational materials design and development. [ABSTRACT FROM AUTHOR]

Fundamental in building any materials database is the capability to describe the materials whose data are contained therein accurately. While many systems exist for describing traditional materials, such as metals, polymers, ceramics, and others, the evolving field of nanotechnology presents new challenges. In this paper, we define the goals of a materials description system and the information categories used to describe traditional materials. We then discuss the challenges presented by materials on the nanoscale and suggest ways of overcoming those challenges.

Several challenges are involved in developing and maintaining materials property databases, including improvements in measurement procedures, the changing nature of materials, access to proprietary data on new materials, and the need for quality evaluation. In this paper we discuss each of these issues and their impact on the availability of high quality material property data, using ceramics as an example material.

Fundamental in building any materials database is the capability to describe the materials whose data are contained therein accurately. While many systems exist for describing traditional materials, such as metals, polymers, ceramics, and others, the evolving field of nanotechnology presents new challenges. In this paper, we define the goals of a materials description system and the information categories used to describe traditional materials. We then discuss the challenges presented by materials on the nanoscale and suggest ways of overcoming those challenges. [ABSTRACT FROM AUTHOR]

Many relationships between parameters and physical properties in materials science and engineering are represented as mathematical expressions, such as empirical equations and regression expressions. Some materials databases handle such information with indirect methods: as a table of sets of parameters, as a list of statements of programming languages, and other ways. There is no standardized way to represent mathematical relationships, and that makes it difficult to exchange, process, and display such information. The AIST (National Institute of Advanced Industrial Science and Technology in Japan) thermophysical property database manages sets of parameter values for expressions and Fortran statements that represent relationships between physical parameters, e.g., temperature, pressure, etc. and thermophysical properties. However, in this method, it is not easy to add new parameters, to process expressions, and exchange information with other software tools. In this paper, we describe the current implementation of representing mathematical knowledge in the AIST thermophysical property database, and we also discuss its problems, sample implementations, and definitions of the OpenMath content dictionary for materials science and engineering. [ABSTRACT FROM AUTHOR]

Several challenges are involved in developing and maintaining materials property databases, including improvements in measurement procedures, the changing nature of materials, access to proprietary data on new materials, and the need for quality evaluation. In this paper we discuss each of these issues and their impact on the availability of high quality material property data, using ceramics as an example material. [ABSTRACT FROM AUTHOR]

The Seebeck coefficient is a physical parameter routinely measured to identify the potential thermoelectric performance of a material. However, researchers employ a variety of techniques, conditions, and probe arrangements to measure the Seebeck coefficient, resulting in conflicting materials data. To compare and evaluate these methodologies, and to identify optimal Seebeck coefficient measurement protocols, we have developed an improved experimental apparatus to measure the Seebeck coefficient under multiple conditions and probe arrangements (300 K-1200 K). This paper will describe in detail the apparatus design and instrumentation, including a discussion of its capabilities and accuracy as measured through representative diagnostics. In addition, this paper will emphasize the techniques required to effectively manage uncertainty in high temperature Seebeck coefficient measurements. [ABSTRACT FROM AUTHOR]

Human use of materials is a major driver of global environmental change. The links between materials use and economic development are central to the challenge of decoupling of materials use and economic growth (dematerialization). This article presents a new global material flow dataset compiled for the year 2000, covering 175 countries, including both extraction and trade flows, and comprising four major material categories: biomass, construction minerals, fossil energy carriers and ores/industrial minerals. First, we quantify the variability and distributional inequality (Gini coefficients) in international material consumption. We then measure the influence of the drivers population, GDP, land area and climate. This analysis yields international income elasticities of material use. Finally, we examine the coupling between material flows, and between income and material productivity, measured in economic production per tonne material consumed. Material productivity is strongly coupled to income, and may thus not be suitable as an international indicator of environmental progress — a finding which we relate to the economic inelasticity of material consumption. The results demonstrate striking differences between the material groups. Biomass is the most equitably distributed resource, economically the most inelastic, and is not correlated to any of the mineral materials. The three mineral material groups are closely coupled to each other and economic activity, indicating that the challenge of dematerializing industrial economies may require fundamental structural transformation. Our analysis provides a first systematic investigation of international differences in material use and their drivers, and thus serves as the basis for more detailed future work. [Copyright &y& Elsevier]

After briefly summarizing information about the Central Asian countries and their libraries, the authors discuss the challenges facing the national bibliographic agencies there. In Tajikistan and Turkmenistan, the national bibliographies have ceased. In the other three countries, national bibliographic indexes are published, but only in print format, and they do not cover non-print material. Kazakhstan and Kyrgyzstan are actively working to modernize their national bibliographies. Bibliographers in the other countries are ready to move to international standards and automation, but are hindered by logistical and financial problems. They would benefit greatly from assistance by the international bibliographic community. [ABSTRACT FROM AUTHOR]

The widespread and rapid growth of materials research, together with the desire to shorten design times for commercial products, has increased the importance of numeric databases as ready sources of infor-mation. This article provides the materials engineer and component designer a guide to these sources in-cluding the data available and the points of contact for gaining access. [ABSTRACT FROM AUTHOR]

• MATCOR software for cross-correlation/validation of materials databases. • Identify outliers and common trends between materials databases. • Data curation for materials discovery. Data analytics approaches are increasingly often used to facilitate property-specific materials discovery. The uncertainties in these approaches can be greatly affected by the fidelity of the data sets that are used to train the data models. Therefore, data curation is an essential step for obtaining well-constrained model predictions. This can be a challenging task, especially for data sets that are too large for human quality control. We developed MATCOR, an open source, user-friendly, easily adaptable software to facilitate the data curation process. MATCOR processes lists of material identifiers in either AFLOW or Materials Project format and searches for the best matching materials entry in the other database. This is a non-trivial task due to differences in labeling and/or non-unique usage of material labels. MATCOR uses a combination of characteristics such space group, compound formula, crystal structure and use of Hubbard-U to provide the best possible comparison between databases. The capabilities of MATCOR are demonstrated for density, elastic properties, magnetic properties, and band gap correlations between AFLOW and Materials Project. We find that density shows the highest correlation among the tested properties, 93% of verified densities agree to within ±2%. Bulk- and shear-moduli showed deviations of less than ±10% for 80.6% and 65.1% of the materials, respectively. The classification of materials as non-magnetic/paramagnetic and metallic/gapped are consistent among the two databases for 91% and 69% of the materials, respectively. These examples show that MATCOR can be used to automate and thereby accelerate the data curation process prior to materials discovery through data analytical models. [ABSTRACT FROM AUTHOR]

A calendar of events for the metal industry from July-December 2016 is presented which include conferences on materials data infrastructure, slag metallurgy and energy processes modeling.

The article presents the author's views on the works of the Minerals, Metals and Materials Society (TMS) and his passion for the works of fictional detective Sherlock Holmes. Various topics discussed include education of materials data infrastructure, leadership in TMS and convocation organized by the National Academy of Engineering.

Abstract: In recent years, material development for ITER blanket components has been fervent; yet, a lack of materials data has been identified by the ITER International Organization (IO) and other ITER parties. Therefore, extensive work on assessment of materials data and qualification of the materials with all the relevant interfaces (joints) has been performed to ensure that requirements are fulfilled for the ITER operational conditions with acceptable margins for the foreseen lifetime of the ITER project. This paper will provide an overview of this qualification program with examples of recent results within the scope of the European contribution of blanket components to ITER. Ongoing actions to achieve a comprehensive understanding of the three main materials, beryllium, CuCrZr and stainless steel grade 316L(N)-IG, and their joints included in the blankets are discussed. [Copyright &y& Elsevier]

How do we go about harnessing the “Big Data” paradigm? [ABSTRACT FROM AUTHOR]