3 papers
cond-mat.mtrl-sci2026
Predicting Atomistic Transitions with Transformers
Henry Tischler, Wenting Li, Qi Tang +2
Accurate knowledge of the atomistic transition pathways in materials and material surfaces is crucial for many material science problems. However, conventional simulation technique…
cond-mat.stat-mech2026
Accelerating Multicanonical Sampling with Irreversibility
Thomas Vogel, Ying Wai Li
Flat-histogram Monte Carlo simulations are well-established, robust methods to perform random walks in a physical observable or parameter space, making them suitable for finding gr…
cond-mat.soft2025
Confusion-driven machine learning of structural phases of a flexible, magnetic Stockmayer polymer
Dilina Perera, Samuel McAllister, Joan Josep Cerdà +1
We use a semi-supervised, neural-network based machine learning technique, the confusion method, to investigate structural transitions in magnetic polymers, which we model as chain…