6 papers · 1 filter
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…
Derivative Informed Learning of Exchange-Correlation Functionals
Eike S. Eberhard, Luca A. Thiede, Abdul Aldossary +5
Machine-learned (ML) exchange-correlation (XC) functionals aim to replace human-designed density functional approximations by learning directly from reference data, but they still…
MÅLe-Î: Learning the Coupled-Cluster Response State for Energies, Gradients, and Properties
Andreas Burger, Luca Thiede, Abdulrahman Aldossary +4
Coupled-cluster (CC) theory is often considered the gold standard of quantum chemistry, but its high computational cost limits routine access to accurate energies, forces and respo…
Coupled Cluster con MÅLe: Molecular Orbital Learning for Neural Wavefunctions
Luca Thiede, Abdulrahman Aldossary, Andreas Burger +9
Density functional theory (DFT) is the most widely used method for calculating molecular properties; however, its accuracy is often insufficient for quantitative predictions. Coupl…
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…
DEQuify your force field: More efficient simulations using deep equilibrium models
Andreas Burger, Luca Thiede, Alán Aspuru-Guzik +1
Machine learning force fields show great promise in enabling more accurate molecular dynamics simulations compared to manually derived ones. Much of the progress in recent years wa…