Researchers have developed CatRetriever, a novel contrastive representation learning model designed to bridge the gap between catalyst surface structures and their corresponding bulk materials. This model aligns slab and bulk crystal representations in a shared latent space, enabling accurate retrieval of plausible parent bulk candidates from slab queries. The framework has been extended into a pipeline for discovering bulk catalysts optimized for specific adsorption energies, considering both structural compatibility and target adsorption ranges. AI
IMPACT Enhances AI-driven materials discovery by enabling the connection of surface-level catalyst designs to bulk properties.
RANK_REASON The cluster contains a research paper detailing a new AI model for catalyst discovery.
- Adsorption energy of nano- and microparticles at liquid-liquid interfaces
- Bulk
- Catalyst Discovery into Practice
- CatRetriever
- Crystallographic symmetry and magnetic structure of CoO
- Formation energy and photoelectrochemical properties of BiVO4after doping at Bi3+or V5+sites with higher valence metal ions
- heterogeneous catalysis
- Latent.Space
- plate
- surface energy
- synthesizability
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