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 learning reshapes our comprehension and manipulation of materials by accelerating discovery and enabling tailored design through property prediction models and structure-property relationships. Machine learning extends its influence to image processing, improving object detection, classification, and segmentation precision and enabling methods like image generation, revolutionizing the potential of image processing in materials research. The most recent developments show how machine learning can have a transformative impact at the atomic level by enabling precise property prediction and intricate data extraction, representing significant advancements in material understanding and innovation. The review highlights how machine learning has the potential to revolutionize materials research by accelerating discovery, improving performance, and stimulating innovation. It does so while acknowledging obstacles like poor data quality and complicated algorithms. Machine learning offers a wide range of exciting prospects for scientific investigation and technological advancement, positioning it as a powerful force for influencing the future of materials research.