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2018
DOI: 10.1007/s10845-018-1392-0
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Materials informatics

Abstract: Materials informatics employs techniques, tools, and theories drawn from the emerging fields of data science, internet, computer science and engineering, and digital technologies to the materials science and engineering to accelerate materials, products and manufacturing innovations. Manufacturing is transforming into shorter design cycles, mass customization, ondemand production, and sustainable products. Additive manufacturing or 3D printing is a popular example of such a trend. However, the success of this … Show more

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Cited by 105 publications
(66 citation statements)
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References 63 publications
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“…Typically, the features are encoded with structure and property parameters, such as electronic properties (band gap, dielectric constant, work function, electron density, electron affinity, etc), structure properties (atomic radial distribution functions, configuration, property‐labeled materials fragments, Voronoi tessellations, etc) and magnetic properties. Reasonable selection of features is difficult and expensive . In previous investigations, researchers selected different dimensions and types of features to build different ML models and adopted the model with the best performance.…”
Section: Basic Procedures Of ML In Materials Sciencementioning
confidence: 99%
“…Typically, the features are encoded with structure and property parameters, such as electronic properties (band gap, dielectric constant, work function, electron density, electron affinity, etc), structure properties (atomic radial distribution functions, configuration, property‐labeled materials fragments, Voronoi tessellations, etc) and magnetic properties. Reasonable selection of features is difficult and expensive . In previous investigations, researchers selected different dimensions and types of features to build different ML models and adopted the model with the best performance.…”
Section: Basic Procedures Of ML In Materials Sciencementioning
confidence: 99%
“…Most of previous research works use the abovementioned process (Figure a) to predict the nanomaterials performance, where feature engineering is indispensable, but difficult and expensive . This working paradigm can be regarded as the first‐generation approach.…”
Section: New Trends Of Machine Learning In Nanomaterials Discovery Anmentioning
confidence: 99%
“…Pearson and MIC approaches can provide rankings based on quantitative analysis to determine a threshold score for feature selection. The other and more critical aspect of correlation analysis is the validation/generation of materials hypotheses [24]. If highly-ranked features are already known and generally accepted, then correlation analysis can confirm the already established mechanisms.…”
Section: Correlation Analysis Between Considered Features and Afa Lmpsmentioning
confidence: 99%