An Overview on the Role of Artificial Intelligence in Modern Advancements of Material Science
Abstract
Artificial intelligence (AI) has become a disruptive force in many industries over the past few decades, and the subjects of material science and engineering are no exception. This review examines the tremendous effects of AI on material innovation, design, prediction, and synthesis. The time-consuming and ineffective nature of conventional trial-and-error procedures in material research is well known. However, AI has ushered in a new era of increased productivity owing to the abundance of training data, improved computer power, and sophisticated deep learning algorithms. The use of AI in material design is distinguished by its capacity to identify complex correlations and patterns within large datasets, allowing the prediction of material characteristics and behaviors. In addition, AI-driven models are excellent at predicting the performance of materials, which is crucial in sectors where exact material specifications are required, and AI has an impact on material synthesis in addition to design and forecasting. It manages and optimizes the synthesis procedure to effectively generate materials with the desired qualities. This study focuses on the specific methodologies and advantages of AI over traditional approaches in applications related to material science. The speed at which AI can scan enormous datasets, find hidden patterns, and adjust to new information dramatically reduces research costs and timetables. This paper explores the development of AI in material science in the future, including how algorithms can be improved by integrating exceedingly intricate simulations. The future of quick innovation and discovery is promising because of the synergy between AI and material science. Finally, this page explores potential challenges and pitfalls in using AI in Material Science, including ethical and technical considerations.