Opinion Article
Harnessing the power of machine learning in materials synthesis and characterization
Kiran Subedi  
Director, Analytical Services Laboratory Department, North Carolina A&T State University, United States

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In the field of materials science, an enduring imperative exists for the investigation of pioneering materials, encompassing a diverse array ranging from graphene to shape-memory alloys, as well as bioengineered materials distinguished by their extraordinary attributes [1]. These materials hold the potential to incite transformative advancements across a broad spectrum of industries, encompassing domains as varied as electronics, healthcare, and beyond. In response to this exigent need, researchers are progressively charting a course toward a somewhat unconventional collaborator: machine learning. The harmonious convergence of machine learning and materials science has ushered in a noteworthy era marked by substantial materials synthesis and characterization advancements. This convergence portends the arrival of a promising epoch characterized by an intensified focus on innovation and the unearthing of discoveries [2].