Ontologies and Databases – Knowledge Engineering for Materials Informatics

Glick, J

doi:10.1016/b978-0-12-394399-6.00008-4

Cited by 7 publications

(11 citation statements)

References 20 publications

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“…The increasing gap between data, information, knowledge, and utility, which calls for more efficient approaches to accelerate this conversion. Adapted from [21], copyright 2013 with permission from Elsevier. faster predictions of properties and crystalline structures given the vast amount of available physical and chemical data via ML algorithms.…”

Section: Fundamentals Of Methodsmentioning

confidence: 99%

“…Moreover, converting this knowledge for the benefit of society, which is the ultimate goal, is an even larger challenge. In figure 5, Glick [21] represents these ideas as gaps between data creation and storage, and the capability to obtain knowledge and usable technologies. The tendency of this gap is to increase over time.…”

Section: Development Of Computational Materials Sciencementioning

confidence: 99%

“…In a rigorous definition, HT concerns the high-volume data generation step, whereas screening or mining process refers to the application of constraints to the database in order to filter or select the best candidates according to the desired attributes. The database is generally screened in sequence through a funnellike approach, where materials satisfying each constraint pass to the next step, while those who fail to meet one or more of them are eliminated [21]. A final step may be to evaluate what characteristics make the top candidates perform best in the desired property, and then predict if these features can be improved further.…”

Section: (Big Data) Screening and Miningmentioning

confidence: 99%

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From DFT to machine learning: recent approaches to materials science–a review

et al. 2019

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Recent advances in experimental and computational methods are increasing the quantity and complexity of generated data. This massive amount of raw data needs to be stored and interpreted in order to advance the materials science field. Identifying correlations and patterns from large amounts of complex data is being performed by machine learning algorithms for decades. Recently, the materials science community started to invest in these methodologies to extract knowledge and insights from the accumulated data. This review follows a logical sequence starting from density functional theory as the representative instance of electronic structure methods, to the subsequent high-throughput approach, used to generate large amounts of data. Ultimately, data-driven strategies which include data mining, screening, and machine learning techniques, employ the data generated. We show how these approaches to modern computational materials science are being used to uncover complexities and design novel materials with enhanced properties. Finally, we point to the present research problems, challenges, and potential future perspectives of this new exciting field. characterized by its diverse and huge volume, usually ranging from terabytes to petabytes of data, being created in or near real-time. Such data is found either structured and unstructured in nature, and is exhaustive, usually aiming to capture entire populations in a scalable manner [7]. Simple tasks represent challenges in this scale: capture, curation, storage, search, sharing, analysis, and visualization of the data cannot be accomplished without the proper tools. Thus, it can be effectively summarized by the popular 'five V's': volume, velocity, variety, veracity, and value, shown in figure 3(right). A related sixth V is visualization, although not exclusive to Big Data, which requires different techniques to handle data with various characteristics.Striving to tackle the challenges imposed by Big Data, the field of Data Science has arisen. It is largely interdisciplinary being a combination of mathematics and statistics, computer science and programming, and domain knowledge for problem definition and solving, as shown in figure 3(left). Its objective is, roughly speaking, to deal with the whole process of data production, cleaning, preparation, and finally, analysis. Data science encompasses areas such as Big Data, which deals with large volumes of data, and data mining, which relates to analysis processes to discover patterns and extract knowledge from data, part of the so-called Knowledge Discovery in Databases (KDD).The analysis process within Data Science is challenging, as the techniques are very different from traditional static and rigid datasets, generated and analyzed under a predetermined hypothesis. The distinction from traditional data is based on the larger abundance, exhaustivity, and variety of Big Data. It is also much more dynamic, messy and uncertain, being highly relational [7]. Recently, the possibility of overcoming such a challenge slowly sta...

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Section: Fundamentals Of Methodsmentioning

confidence: 99%

Section: Development Of Computational Materials Sciencementioning

confidence: 99%

Section: (Big Data) Screening and Miningmentioning

confidence: 99%

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From DFT to machine learning: recent approaches to materials science–a review

et al. 2019

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“…A database represents the simplest and shortest path to measurable outcomes, but not necessarily to valuable ones. 11 A further problem with a database is that its description of the world is closed. If an item is not present in the database, it is deemed not to exist.…”

Section: Database or Ontology?mentioning

confidence: 99%

“…This is crossed using two transformations: abstraction (essentially, removing context) and connectivity (associating items using expressions of logic). 11 Transformation from information to knowledge depends on the adequacy and correctness of the abstraction methods, rather than mechanical criteria such as the amount of available data or the speed of the search.…”

Section: Database or Ontology?mentioning

confidence: 99%

The trade-off: a central concept for biomimetics

VincentJulian

2017

Bioinspired, Biomimetic and Nanobiomaterials

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Biology is gradually yielding lessons and ideas for technology, but the resulting innovation is adventitious. Biology is also very complex: currently with no underlying analytical model and so cannot adequately be interrogated by technologists. A concept which can bridge this gap is the trade-off, which leads to speciation in biology and aspects of design and problem-solving in engineering. An ontology is described which uses biological organisms as case studies. Terms have been adapted from TRIZ (the Russian system for solving problems creatively) to define trade-offs and the factors by which they can be manipulated. As an example the ontology resolves the biological trade-off "speed-accuracy", yielding factors for a biomimetic version of the trade-off. These are Feedback (e.g. errorcorrection), Dynamic Response (e.g. control of thresholds), Adaptation (e.g. ability to predict) and Consolidation (e.g. stochastic accumulation). Since much of evolution can be related to the resolution of trade-offs, it is expected that this ontology can also categorise aspects of natural selection. Multi-criteria analysis will allow more versatile numerical models to be developed.

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Exploring the mathematic equations behind the materials science data using interpretable symbolic regression

Wang,

et al. 2024

Interdisciplinary Materials

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Symbolic regression (SR), exploring mathematical expressions from a given data set to construct an interpretable model, emerges as a powerful computational technique with the potential to transform the “black box” machining learning methods into physical and chemistry interpretable expressions in material science research. In this review, the current advancements in SR are investigated, focusing on the underlying theories, fundamental flowcharts, various techniques, implemented codes, and application fields. More predominantly, the challenging issues and future opportunities in SR that should be overcome to unlock the full potential of SR in material design and research, including graphics processing unit acceleration and transfer learning algorithms, the trade‐off between expression accuracy and complexity, physical or chemistry interpretable SR with generative large language models, and multimodal SR methods, are discussed.

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Ontologies and Databases – Knowledge Engineering for Materials Informatics

Cited by 7 publications

References 20 publications

From DFT to machine learning: recent approaches to materials science–a review

From DFT to machine learning: recent approaches to materials science–a review

The trade-off: a central concept for biomimetics

Exploring the mathematic equations behind the materials science data using interpretable symbolic regression

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