as someone working at the intersection of machine learning and materials research, I’ve seen how computational models can drastically accelerate discovery by narrowing the experimental search space and uncovering non-intuitive patterns.
Though I have not done enough studies on this matter . The doubt still remains inside of me about how this computational knowledges translate in real life. I would love to know how you came to the conclusion that it can actually accelerate discovery.