249 citations · 546 across the 22 of their papers we have counts for
28 papers
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…
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…
GO-Diff: Data-free and amortized global structure optimization
Nikolaj Rønne, Tejs Vegge, Arghya Bhowmik
We introduce GO-Diff, a diffusion-based method for global structure optimization that learns to directly sample low-energy atomic configurations without requiring prior data or exp…
HIP: Hessian Interatomic Potentials without derivatives
Andreas Burger, Luca Thiede, Nikolaj Rønne +5
Molecular Hessians, the second derivatives of the potential energy, are fundamental to many workflows in computational chemistry. Usually, accurate Hessians are computationally exp…
Dis-GEN: Disordered crystal structure generation
Martin Hoffmann Petersen, Ruiming Zhu, Haiwen Dai +6
A wide range of synthesized crystalline inorganic materials exhibit compositional disorder, where multiple atomic species partially occupy the same crystallographic site. As a resu…
Kinetic Langevin Diffusion for Crystalline Materials Generation
François Cornet, Federico Bergamin, Arghya Bhowmik +3
Generative modeling of crystalline materials using diffusion models presents a series of challenges: the data distribution is characterized by inherent symmetries and involves mult…
Acoustic Classification of Maritime Vessels using Learnable Filterbanks
Jonas Elsborg, Tejs Vegge, Arghya Bhowmik
Reliably monitoring and recognizing maritime vessels based on acoustic signatures is complicated by the variability of different recording scenarios. A robust classification framew…
ELECTRA: A Cartesian Network for 3D Charge Density Prediction with Floating Orbitals
Jonas Elsborg, Luca Thiede, Alán Aspuru-Guzik +2
We present the Electronic Tensor Reconstruction Algorithm (ELECTRA) - an equivariant model for predicting electronic charge densities using floating orbitals. Floating orbitals are…
A foundation model for atomistic materials chemistry
Ilyes Batatia, Philipp Benner, Yuan Chiang +85
Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much…
Reducing Training Data Needs with Minimal Multilevel Machine Learning (M3L)
Stefan Heinen, Danish Khan, Guido Falk von Rudorff +7
For many machine learning applications in science, data acquisition, not training, is the bottleneck even when avoiding experiments and relying on computation and simulation. Corre…
Transition1x -- a Dataset for Building Generalizable Reactive Machine Learning Potentials
Mathias Schreiner, Arghya Bhowmik, Tejs Vegge +2
Machine Learning (ML) models have, in contrast to their usefulness in molecular dynamics studies, had limited success as surrogate potentials for reaction barrier search. It is due…
NeuralNEB -- Neural Networks can find Reaction Paths Fast
Mathias Schreiner, Arghya Bhowmik, Tejs Vegge +2
Quantum mechanical methods like Density Functional Theory (DFT) are used with great success alongside efficient search algorithms for studying kinetics of reactive systems. However…
Phase Separating Electrode Materials Chemical Inductors
Klemen Zelič, Igor Mele, Arghya Bhowmik +1
We discover presence of chemical inductive effects in phase separating ion intercalation energy storage materials, specifically in lithium iron phosphate (LFP) and also lithium tit…
Wave function Ansatz (but Periodic) Networks and the Homogeneous Electron Gas
Max Wilson, Saverio Moroni, Markus Holzmann +4
We design a neural network Ansatz for variationally finding the ground-state wave function of the Homogeneous Electron Gas, a fundamental model in the physics of extended systems o…
Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
Peter Bjørn Jørgensen, Arghya Bhowmik
Electron density is the fundamental variable in the calculation of ground state energy with density functional theory (DFT). Beyond total energy, features and changes…
Calibrated Uncertainty for Molecular Property Prediction using Ensembles of Message Passing Neural Networks
Jonas Busk, Peter Bjørn Jørgensen, Arghya Bhowmik +3
Data-driven methods based on machine learning have the potential to accelerate computational analysis of atomic structures. In this context, reliable uncertainty estimates are impo…