Scope of machine learning in materials research—A review

• DOI: 10.1016/j.apsadv.2023.100523
• Publication Date: 2023-11-28T18:38:30Z
• Authors: Md Hosne Mobarak; Mariam Akter Mimona; Md. Aminul Islam; Nayem Hossain; Fatema Tuz Zohura; Ibnul Imtiaz; Md Israfil Hossai...
• Keywords: Transformative Learning; Scope (computer science)

This comprehensive review investigates the multifaceted applications of machine learning in materials research across six key dimensions, redefining field's boundaries. It explains various knowledge acquisition mechanisms starting with supervised, unsupervised, reinforcement, and deep techniques. These techniques are transformative tools for transforming unactionable data into insightful actions. Moving on to synthesis, emphasizes profound influence learning, as demonstrated by predictive models that speed up material selection, structure-property relationships reveal crucial connections, data-driven discovery fosters innovation. Machine reshapes our comprehension manipulation accelerating enabling tailored design through property prediction relationships. extends its image processing, improving object detection, classification, segmentation precision methods like generation, revolutionizing potential processing research. The most recent developments show how can have a impact at atomic level precise intricate extraction, representing significant advancements understanding highlights has revolutionize discovery, performance, stimulating does so while acknowledging obstacles poor quality complicated algorithms. offers wide range exciting prospects scientific investigation technological advancement, positioning it powerful force influencing future