197 citations
- Helmholtz-Zentrum Dresden-RossendorfDE168 papers
- Technische Universität DresdenDE42 papers
- University of WrocławPL25 papers
- Christian-Albrechts-Universität zu KielDE19 papers
- University of RostockDE19 papers
- KTH Royal Institute of TechnologySE16 papers
- Sandia National LaboratoriesUS13 papers
- European X-Ray Free-Electron LaserDE11 papers
- Lawrence Livermore National LaboratoryUS11 papers
- University of OxfordGB9 papers
- Commissariat à l'Énergie Atomique et aux Énergies AlternativesFR8 papers
- Al-Farabi Kazakh National UniversityKZ7 papers
5 papers · 2 filters
Machine Learning-Driven Structure Prediction for Iron Hydrides
Hossein Tahmasbi, Kushal Ramakrishna, Mani Lokamani +1
We created a computational workflow to analyze the potential energy surface (PES) of materials using machine-learned interatomic potentials in conjunction with the minima hopping a…
Magnetic State Control of Non-van der Waals 2D Materials by Hydrogenation
Tom Barnowsky, Stefano Curtarolo, Arkady V. Krasheninnikov +2
Controlling the magnetic state of two-dimensional (2D) materials is crucial for spintronic applications. By employing data-mining and autonomous density functional theory calculati…
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…
How to verify the precision of density-functional-theory implementations via reproducible and universal workflows
Emanuele Bosoni, Louis Beal, Marnik Bercx +42
In the past decades many density-functional theory methods and codes adopting periodic boundary conditions have been developed and are now extensively used in condensed matter phys…
Transferable Interatomic Potentials for Aluminum from Ambient Conditions to Warm Dense Matter
Sandeep Kumar, Hossein Tahmasbi, Kushal Ramakrishna +5
We present a study on the transport and materials properties of aluminum spanning from ambient to warm dense matter conditions using a machine-learned interatomic potential (ML-IAP…