115 citations · 272 across the 12 of their papers we have counts for
12 papers
MANDALA: An E(3)-Equivariant Graph Neural Network Framework for Learning Electronic-Structure Operators with Observable Guidance
Bartosz Brzoza, Wiktoria Szopa, Zakaria Elabid +5
Electronic-structure calculations based on Kohn-Sham density functional theory remain indispensable in computational materials science and chemistry. Their computational cost, howe…
Molecules Meet Language: Confound-Aware Representation Learning and Chemical Property Steering in Transformer-VAE Latent Spaces
Zakaria Elabid, Jan Andrzejewski, Bartosz Brzoza +1
Molecular generative models often assume meaningful latent geometry, but apparent property predictability can reflect sequence-level shortcuts rather than chemical organization. We…
Scalable Machine Learning Model for Energy Decomposition Analysis in Aqueous Systems
Hossein Tahmasbi, Michael Beerbaum, Bartosz Brzoza +2
Energy decomposition analysis (EDA) based on absolutely localized molecular orbitals provides detailed insights into intermolecular bonding by decomposing the total molecular bindi…
Machine Learning Time Propagators for Time-Dependent Density Functional Theory Simulations
Karan Shah, Attila Cangi
Time-dependent density functional theory (TDDFT) is a widely used method to investigate electron dynamics under external time-dependent perturbations such as laser fields. In this…
Materials Learning Algorithms (MALA): Scalable Machine Learning for Electronic Structure Calculations in Large-Scale Atomistic Simulations
Attila Cangi, Lenz Fiedler, Bartosz Brzoza +11
We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for…
Accelerating Electron Dynamics Simulations through Machine Learned Time Propagators
Karan Shah, Attila Cangi
Time-dependent density functional theory (TDDFT) is a widely used method to investigate electron dynamics under various external perturbations such as laser fields. In this work, w…
Machine learning the electronic structure of matter across temperatures
Lenz Fiedler, Normand A. Modine, Kyle D. Miller +1
We introduce machine learning (ML) models that predict the electronic structure of materials across a wide temperature range. Our models employ neural networks and are trained on d…
Physics-Informed Neural Networks as Solvers for the Time-Dependent Schrödinger Equation
Karan Shah, Patrick Stiller, Nico Hoffmann +1
We demonstrate the utility of physics-informed neural networks (PINNs) as solvers for the non-relativistic, time-dependent Schrödinger equation. We study the performance and genera…
Predicting electronic structures at any length scale with machine learning
Lenz Fiedler, Normand A. Modine, Steve Schmerler +5
The properties of electrons in matter are of fundamental importance. They give rise to virtually all molecular and material properties and determine the physics at play in objects…
Training-free hyperparameter optimization of neural networks for electronic structures in matter
Lenz Fiedler, Nils Hoffmann, Parvez Mohammed +6
A myriad of phenomena in materials science and chemistry rely on quantum-level simulations of the electronic structure in matter. While moving to larger length and time scales has…
A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry
Lenz Fiedler, Karan Shah, Michael Bussmann +1
With the growth of computational resources, the scope of electronic structure simulations has increased greatly. Artificial intelligence and robust data analysis hold the promise t…
Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks
J. Austin Ellis, Lenz Fiedler, Gabriel A. Popoola +5
We present a numerical modeling workflow based on machine learning (ML) which reproduces the the total energies produced by Kohn-Sham density functional theory (DFT) at finite elec…