New ML dataset accelerates catalysis research in 2D MXenes

Researchers have developed a new benchmark dataset and machine learning models to accelerate the study of catalysis in 2D MXenes. By combining density functional theory calculations with machine learning interatomic potentials, they achieved a significant speed-up in predicting atomic forces and formation energies. This approach, validated with models like EquiformerV2 and MACE, maintains high accuracy and promises more efficient exploration of MXene catalytic properties. AI

IMPACT Accelerates discovery of new catalytic materials by enabling faster simulations.

RANK_REASON The cluster contains an academic paper detailing a new benchmark dataset and machine learning models for materials science research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• EquiformerV2
• MACE
• MatRIS
• MXenes

• paper
• product

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