From generating candidates to scaling discovery: the future of scientific ML
ctjlewis · x · 2026-10-23
A machine learning engineer who has spent years training specialized models across scientific domains publishes a long-form article on the future of scientific machine learning.
The piece traces the field's evolution from single-point tasks like generating candidate molecules/materials toward AI-driven discovery at scale, drawing on hands-on cross-domain modeling experience to discuss bottlenecks and what comes next.
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