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New ML dataset accelerates catalysis research in 2D MXenes

By PulseAugur Editorial · [1 sources] ·

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 →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New ML dataset accelerates catalysis research in 2D MXenes

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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]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Pavlo Melnyk, Anmar Karmush, M{\aa}rten Wadenb\"ack, Ania Beatriz Rodr\'iguez-Barrera, Johanna Rosen, Michael Felsberg, Jonas Bj\"ork ·

    Benchmark Dataset for Catalysis on 2D MXenes

    arXiv:2606.00794v1 Announce Type: cross Abstract: Merging first-principles calculations with machine learning (ML), we aim to accelerate the exploration of catalytic behaviour in novel materials. We focus on two-dimensional (2D) Ti$_2$CT$_y$ MXenes, whose versatile surface chemis…

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