87 citations · 105 across the 3 of their papers we have counts for
Showing cond-mat.mtrl-sciShow all
2 papers · 1 filter
cond-mat.mtrl-sci2026
Dynamically training machine-learning-based force fields for strongly anharmonic materials
Martin Callsen, Tai-Ting Lee, Mei-Yin Chou
Machine learning (ML) force fields have emerged as a powerful tool for computing materials properties at finite temperatures, particularly in regimes where traditional phonon-based…
cond-mat.mtrl-sci2023★ 87 cited
How to verify the precision of density-functional-theory implementations via reproducible and universal workflows
Emanuele Bosoni, Louis Beal, Marnik Bercx +42
In the past decades many density-functional theory methods and codes adopting periodic boundary conditions have been developed and are now extensively used in condensed matter phys…