Closing the synthesis gap in computational materials design (opens in new tab)

Data-driven strategies are reshaping computational materials design by accelerating the prediction of compounds with targeted functionalities. Beyond high-throughput screening, the integration of generative artificial intelligence enables exploration across vast chemical spaces comprising millions of known and hypothetical materials. This abundance of candidates presents a challenge: identifying which candidate compounds are not only low in energy but also synthetically accessible. Here we as...