12 papers
HIP: Hessian Interatomic Potentials without derivatives
Andreas Burger, Luca Thiede, Nikolaj Rønne +6
Molecular Hessians, the second derivatives of the potential energy, are fundamental to many workflows in computational chemistry. Usually, accurate Hessians are computationally exp…
Global Plane Waves From Local Gaussians: Periodic Charge Densities in a Blink
Jonas Elsborg, Felix Ãrtebjerg, 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…
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 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…
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…