249 citations · 546 across the 22 of their papers we have counts for
9 papers · 1 filter
Topo-Spectral Percolation Descriptors for Mechanistic Ion Transport Pathways from Static Crystal Structures
Diptendu Roy, Chiku Parida, Juan María García-Lastra +1
Ion transport controls the macroscopic performance of solid electrolytes, battery electrodes, ion-selective membranes, electrolysis ceramics, mixed conductors, porous sorbents, and…
Let CSP Be Your ANCHOR: Adaptive Crystal Search over Frozen Structure Priors
Emma Lei Hovmand, Jonas Elsborg, Melih Kandemir +1
De novo crystal generation (DNG) models decide where to search in composition space and how to generate structures with one set of weights. We argue that discovery is better served…
Improved Learning of Molecular Energetics Through an Electron-Wise Joint Charge Density and Energy Objective
Vadim Ionas, Jonas Elsborg, Felix Ærtebjerg +1
We present a graph neural network-based learning framework trained to jointly predict electron densities and molecular energies. The model is trained to predict electron densities…
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) enable simulations of larger atomistic systems over longer timescales than traditional ab initio methods but require large, diverse t…
AtomComposer: Discovering Chemical Space from First Principles with Reinforcement Learning
Bjarke Hastrup, Francois Cornet, Tejs Vegge +1
Discovering novel stable molecules without training data remains a grand scientific challenge. Current molecular generative models are trained on large, pre-curated datasets, which…
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 Configurational Charge Ordering 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…