This article critiques the limitations of artificial intelligence in advancing material science, arguing that while AI has shown promise in generating material predictions and theoretical breakthroughs, these have yet to translate into practical, real-world applications. It highlights the gap between computational discoveries and their experimental validation, emphasizing that many AI-predicted materials lack industrial relevance or scalability. The challenges lie in integrating AI models with the physical constraints and complexities of material synthesis and application. Despite AI’s potential to revolutionize material discovery, its contributions remain largely conceptual, underscoring the need for closer collaboration between AI researchers and experimental scientists to achieve tangible progress.
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Best regards,
Dr. Gabriele Mogni
Technical Consultant and EU Representative
Virtual Lab Inc., the parent company of the Materials Square platform
Website: Home | Virtual Lab Inc.
Email: gabriele@simulation.re.kr
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