cond-mat.mtrl-sci · 2026-07-22 · No. 61
Materials Science, 2026-07-22.
2 new papers in cond-mat.mtrl-sci. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
2 entries-
01
ATLAS: A Foundation Neural Sampler for Amorphous Materials
Mouyang Cheng, Denis Blessing, Botao Yu, Gerhard Neumann, Mingda Li, Carles Domingo-Enrich, Yuanqi Du
cond-mat.mtrl-sci · cs.LG · physics.comp-ph
Amorphous materials exhibit exceptional mechanical and functional properties, yet their rugged energy landscapes are notoriously difficult to sample. Below the glass-transition temperature, conventional molecular dynamics and Monte Carlo become inefficient because equilibration relies on rare barrier-crossing events, while data-driven generative models are constrained by scarce and biased reference ensembles. Here, we introduce ATLAS, an...
-
02
GQD-AdsNet: Graph Neural Networks Unlock Rapid Exploration of Transition Metal Adsorption on Graphene Quantum Dots
Lara Goncebat, Rodrigo Echeveste, Matías Gerard, Frederik Tielens, Gustavo Belletti, Paola Quaino
cond-mat.mtrl-sci · cs.LG
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. However, the design and characterization of these materials through first-principles calculations are computationally expensive, limiting the exploration of a large number of possible configurations. Here, we developed a framework based on graph neural networks...
This edition is part of The Daily Abstract — cond-mat.mtrl-sci archive. Subscribe to receive these in your inbox each morning, automatically translated to Spanish, with reply-to-PDF: arxivdaily.ignorelist.com.
#D99C5E. Built and served on an always-free VM. The masthead is set 14% letterspaced because newspapers do that and it works.