AI Co-Scientist

TL;DR for operators Lab automation is not the story here. The story is search discipline. The paper introduces HACO, a human–AI co-discovery system that tries to develop a better crystal structure prediction algorithm by searching across generative-modeling ideas, coding candidates, training them, evaluating them, and refining the winners. The system identifies masked generative modeling, specifically MaskGIT from computer vision, as a transferable idea for crystal structure prediction. With sparse human steering, it turns that idea into MaskGXT, a masked discrete-token transformer for generating crystal structures from compositions.1 ...

A laboratory is not a spreadsheet with a sink. That is the small but expensive fact many AI-for-science stories politely step around. Models can rank genes, design proteins, summarise papers, draft protocols, and produce the usual confident parade of mechanistic hypotheses. Then a human still has to seed the cells, choose the pipette, avoid contaminating the plate, notice that an incubation step was skipped, and remember the trick that never made it into the protocol because, apparently, civilisation runs on tacit knowledge and Post-it notes. ...