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Record W4386557891 · doi:10.1002/mgea.9

<i>Materials Genome Engineering Advances</i>: A new journal dedicated to digital and intelligent materials research and development

2023· article· en· W4386557891 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMaterials Genome Engineering Advances · 2023
Typearticle
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsnot available
Fundersnot available
KeywordsPaceBig dataComputer scienceData scienceSoftware deploymentNanotechnologySoftware engineeringMaterials science

Abstract

fetched live from OpenAlex

On behalf of the editorial team, I am delighted to announce the inauguration of the journal, Materials Genome Engineering Advances co-published by Wiley, University of Science and Technology Beijing and Chinese Materials Research Society. Materials Genome Engineering Advances 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. Materials research and development (R&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&D. The advent of the latest scientific and technological advances and industrial revolution demands a substantial transformation in the methodologies used in materials R&D, shifting toward more efficient and innovative modes of operation. To address this grand challenge, materials scientists have introduced the concept of materials genome by drawing an analogy with human genome. 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&D, thereby accelerating the iterative development of new materials and setting a new standard for the field. 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 yet to be a journal dedicated exclusively to MGE research. This lack of a centralized platform stands in stark contrast to the strong demand from authors within this newly established and rapidly evolving research community. Under these significant circumstances, the new journal, Materials Genome Engineering Advances, has been created. The founding of this publication will fill a vital gap, assembling innovative achievements from various MGE-related disciplines and nurturing an interdisciplinary environment and culture. Serving as a centralized platform, it aims to significantly promote the creation of an MGE chain that encompasses high-efficiency computation, advanced experimentation, big data technology, and their integration. By doing so, Materials Genome Engineering Advances will not only accelerate materials R&D but also drive general progress in the broader field of materials science. Materials Genome Engineering Advances strives to become a premier journal in the materials research field. Its aims are threefold: (i) to break the barriers between materials science, computer science, data science, and other related disciplines; (ii) to establish a high-level publishing platform characterized by interdisciplinary collaboration that caters to the evolving requirements of the MGE community; and (iii) to encourage the evolution of digital and intelligent materials R&D. The journal will publish a broad array of topics. These include, but are not limited to, (i) high-efficiency materials computation, such as high-throughput computation, autonomous computation, and integrated computational materials engineering; (ii) advanced materials experimentation techniques, such as high-throughput preparation and characterization, autonomous and intelligent experiments; (iii) data-driven materials science, including materials databases, big-data, and artificial intelligence technologies; and (iv) the application of MGE technologies in materials R&D. By covering this comprehensive spectrum, Materials Genome Engineering Advances aims to facilitate broad-ranging discussions and drive forward-thinking research. Materials Genome Engineering Advances will feature diverse article types, including Research Articles, Reviews, Perspectives, Editorials, Profiles, and Comments. We strongly encourage potential contributors to familiarize themselves with the specific criteria for each article type by referring to the authors' guidelines available at Wiley's Author Guidelines (https://onlinelibrary.wiley.com/page/journal/29409497/homepage/author-guidelines). Furthermore, Materials Genome Engineering Advances embraces an open-access publishing model, ensuring that all published articles are freely accessible through the Wiley Online Library (https://onlinelibrary.wiley.com/journal/29409497). This approach aligns with our commitment to broad dissemination and engagement within the MGE community, thereby fostering collaboration and innovation. With profound gratitude for the robust support from the international research community, we have assembled a strong editorial team for Materials Genome Engineering Advances. It is my pleasure to introduce Prof. Hongbiao Dong (University of Leicester, UK), Prof. Federico Rosei (Institut National de la Recherche Scientifique, Canada), Prof. Isao Tanaka (Kyoto University, Japan), and Prof. Weidong Li (University of Science and Technology Beijing, China) as the associate editors of this journal. These editors, along with our esteemed editorial board members, boast a global academic reputation and bring a wealth of research and editorial experience to our team. Their collective expertise will ensure the publication of papers that encompass the most compelling topics and uphold the highest standards of quality within our journal. At present, the field of MGE is experiencing rapid growth and stands in need of continuous support and investment in many ways. We warmly welcome researchers from diverse fields across the globe to engage with us in this exciting endeavor. Whether as readers, authors, or reviewers of this journal, your collaboration will be instrumental in shaping a more digital and intelligent future for materials science. There is no conflict of interest. Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.267
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it