arXiv Machine Learning By Lara Goncebat (Instituto de Qu\'imica Aplicada del Litoral IQAL), Rodrigo Echeveste (Instituto de Investigaci\'on en Se\~nales, Sistemas e Inteligencia Computacional sinc), Mat\'ias Gerard (Instituto de Investigaci\'on en Se\~nales, Sistemas e Inteligencia Computacional sinc), Frederik Tielens (General Chemistry), Gustavo Belletti (Instituto de Qu\'imica Aplicada del Litoral IQAL), Paola Quaino (Instituto de Qu\'imica Aplicada del Litoral IQAL)

GQD-AdsNet: Graph Neural Networks Unlock Rapid Exploration of Transition Metal Adsorption on Graphene Quantum Dots

Read the original on arXiv Machine Learning →

arXiv:2607. 18591v1 Announce Type: cross Abstract: In recent years, interest in single-atom catalysts supported on carbon-based structures has grown considerably due to their high catalytic activity and efficient uses of metal atoms.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jun 2

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.

By Pavlo Melnyk, Anmar Karmush, M{\aa}rten Wadenb\"ack, Ania Beatriz Rodr\'iguez-Barrera, Johanna Rosen, Michael Felsberg, Jonas Bj\"ork