Scope of machine learning in materials research—A review

This comprehensive review investigates the multifaceted applications of machine learning in materials research across six key dimensions, redefining the field's boundaries. It explains various knowledge acquisition mechanisms starting with supervised, unsupervised, reinforcement, and deep learning techniques. These techniques are transformative tools for transforming unactionable data into insightful actions. Moving on to the materials synthesis, the review emphasizes the profound influence of machine learning, as demonstrated by predictive models that speed up material selection, structure-property relationships that reveal crucial connections, and data-driven discovery that fosters innovation. Machine…