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CheMatE achieves competitive performance by seamlessly integrating chemical structure with natural language understanding, challenging the notion that domain-specific models must sacrifice generality.
SEGO slashes the number of evaluations needed for molecular optimization by 90%, revolutionizing the search for viable drug candidates.
A fully automated LLM framework expands chemical reaction classification from 68 to over 14,000 classes, achieving 97.7% accuracy on unseen data without human curation.
AdsMind achieves a staggering 100% success rate in discovering adsorption configurations while slashing computational costs by 14-fold compared to traditional methods.
Treating chemical affinity as a continuous property reveals a unified explanation for previously ambiguous phenomena in molecule-surface interactions.
