115 citations · 322 across the 8 of their papers we have counts for
8 papers
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…
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…
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…
Accelerating Equilibration in First-Principles Molecular Dynamics with Orbital-Free Density Functional Theory
Lenz Fiedler, Zhandos A. Moldabekov, Xuecheng Shao +4
We introduce a practical hybrid approach that combines orbital-free density functional theory (DFT) with Kohn-Sham DFT for speeding up first-principles molecular dynamics simulatio…
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…
PyFLOSIC: Python-based Fermi-Löwdin orbital self-interaction correction
Sebastian Schwalbe, Lenz Fiedler, Jakob Kraus +3
We present PyFLOSIC, an open-source, general-purpose Python implementation of the Fermi-Löwdin orbital self-interaction correction (FLO-SIC), which is based on the Python simulatio…