Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science
Ankit Agrawal,Alok Choudhary +1 more
TLDR
In this article, the authors look at how data-driven techniques are playing a big role in deciphering processing-structure-property-performance relationships in materials, with illustrative examples of both forward models (property prediction) and inverse models (materials discovery).Abstract:
Our ability to collect “big data” has greatly surpassed our capability to analyze it, underscoring the emergence of the fourth paradigm of science, which is data-driven discovery. The need for data informatics is also emphasized by the Materials Genome Initiative (MGI), further boosting the emerging field of materials informatics. In this article, we look at how data-driven techniques are playing a big role in deciphering processing-structure-property-performance relationships in materials, with illustrative examples of both forward models (property prediction) and inverse models (materials discovery). Such analytics can significantly reduce time-to-insight and accelerate cost-effective materials discovery, which is the goal of MGI.read more
Citations
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Machine learning for molecular and materials science.
TL;DR: A future in which the design, synthesis, characterization and application of molecules and materials is accelerated by artificial intelligence is envisaged.
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A general-purpose machine learning framework for predicting properties of inorganic materials
TL;DR: This manuscript has created a framework capable of being applied to a broad range of materials data, and demonstrates how this new method can be used to predict diverse properties of crystalline and amorphous materials, such as band gap energy and glass-forming ability.
Journal ArticleDOI
Materials discovery and design using machine learning
TL;DR: In this paper, the typical mode of and basic procedures for applying machine learning in materials science are outlined and compared, and the current research status is reviewed with regard to applications of ML in material property prediction, in new materials discovery and for other purposes.
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From DFT to machine learning: recent approaches to materials science–a review
TL;DR: It is shown how data-driven strategies which include data mining, screening, and machine learning techniques, employ the data generated to uncover complexities and design novel materials with enhanced properties.
Journal ArticleDOI
A strategy to apply machine learning to small datasets in materials science
Ying Zhang,Chen Ling +1 more
TL;DR: In three case studies of predicting the band gap of binary semiconductors, lattice thermal conductivity, and elastic properties of zeolites, the integration of crude estimation effectively boosted the predictive capability of machine learning models to state-of-art levels, demonstrating the generality of the proposed strategy to construct accurate ML models using small materials dataset.
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