Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science

By Ankit Agrawal, Alok N. Choudhary. 2016

1,189 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Materials Chemistry

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Citations in the corpus

Learning atoms for materials discovery (2018, background): “, fik ))], [5] where fi stands for the target atom in each pair, and fi1 , fi2 , .”
A Physics-Informed Machine Learning Approach for Solving Heat Transfer Equation in Advanced Manufacturing and Engineering Applications (2020, background): “0 setting where fast and near real-time simulation capabilities are needed (Agrawal and Choudhary, 2016; Erol et al., 2016).”
Transferable Multilevel Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multitask Learning (2020, background): “equation (15), equation (16), and two types of angular functions, Gi4,z1,z2and Gi5,z1,z2, in equation (17) and equation (18), respectively.”
Computational microstructure characterization and reconstruction: Review of the state-of-the-art techniques (2018, background): “As a material’s morphology heavily affects its properties [9,10], the central theme of these frameworks is on inverse materials design by elucidating the link between processing, structure, and properties (aka PSP links) [5,11].”
Roadmap on multiscale materials modeling (2020, methodology): “The adoption of data-driven methods is already happening in the broader field of computational materials science [52], e.”
A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials (2016, neutral): “12 These databases have grown large enough that the discovery of such design rules and models is impractical to accomplish by relying simply on human intuition and knowledge about material behaviour.”