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Data-driven approaches in materials research
Data-driven approaches have suggested novel ways in science and engineering research based on accumulated scientific data with advances in data science, machine learning algorithms, and computing power. Machine learning-assisted, data-driven approaches can provide a comprehensive way to investigate feature-property relationships in material systems with unknown governing equations. Our group uses data-driven approaches along with traditional mechanics-driven approaches in our research to answer scientific questions.
