Materials Science

The article explores whether scientists are born or made. The author reflects on his joy in scientific roles tied to creativity and newness, questioning if this "need for new" is innate or developed through learning and experience.

AI is powerful, but its value depends on data quality and expert oversight. In high-stakes fields, blind trust in AI can be risky. Critical thinking and context still matter.

Scientists' career progression is linked to their professional visibility. This article summarises four archetypes: Not Seen/Not Heard, Heard but Not Seen, Seen but Not Heard, and Seen and Heard. Which one are you?

AI-driven big data is transforming materials science, but a gap remains between data generation and interpretation. To innovate, industries must invest in data literacy, cross-disciplinary training, and collaboration.