Submission Tilte
Artificial Intelligence and Machine Learning Applications in Advanced Materials Science: Design, Characterization, Processing, and Sustainable Development
Submission Abstract:
The integration of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Data-Driven Modeling has transformed materials science by accelerating material discovery, optimization, characterization, and performance prediction. Advanced computational techniques enable researchers to analyze complex material behaviors, predict properties, optimize manufacturing processes, and develop sustainable materials with reduced experimental effort and cost. The emergence of Materials Informatics, Digital Twins, and Explainable AI has further enhanced the capability to design next-generation biomaterials, nanomaterials, composites, ceramics, polymers, semiconductors, and smart materials. This thematic issue aims to provide a comprehensive platform for recent advances in AI-assisted materials research, covering theoretical developments, computational methodologies, experimental validations, industrial applications, and future perspectives toward intelligent and sustainable materials engineering.