249 citations · 284 across the 15 of their papers we have counts for
9 papers · 1 filter
Complete Neural Electronic Initialization Accelerates Materials DFT
Felix Ærtebjerg, Jonas Elsborg, Arghya Bhowmik
We present the first complete machine learning method for accelerating plane-wave density functional theory (DFT) in materials under the projector augmented wave (PAW) formalism. W…
SoLiD26: A First Principles Solid-Liquid Interface Dataset for Machine-learned Interatomic Potentials
Jonas Busk, Emil J. P. Frost, Yogeshwaran Krishnan +7
Machine-learned interatomic potentials (MLIPs) for solid-liquid interfaces in advanced materials applications, e.g., electrochemistry, catalysis and corrosion, require training dat…
Designing dislocation-driven polar vortex networks in twisted perovskites
William Sandholt, Nicolas Gauquelin, John Mangeri +21
Twisting two atomic layers produces a geometric moire pattern, but bonding-induced interfacial reconstruction fundamentally transforms this into an ordered dislocation network - a…
Importance of Electronic Entropy for Machine Learning Interatomic Potentials
Martin Hoffmann Petersen, Steen Lysgaard, Arghya Bhowmik +2
Machine learning interatomic potentials (MLIPs) enable large-scale atomistic simulations but remain challenged in describing mixed-valence materials where charge ordering strongly…
Global Plane Waves From Local Gaussians: Periodic Charge Densities in a Blink
Jonas Elsborg, Felix Ærtebjerg, Luca Thiede +3
We introduce ELECTRAFI, a fast, end-to-end differentiable model for predicting periodic charge densities in crystalline materials. ELECTRAFI constructs anisotropic Gaussians in rea…
Reinforcement Learning for Chemical Ordering in Alloy Nanoparticles
Jonas Elsborg, Emma L. Hovmand, Arghya Bhowmik
We approach the search for optimal element ordering in bimetallic alloy nanoparticles (NPs) as a reinforcement learning (RL) problem and have built an RL agent that learns to perfo…