5 papers
Errors that matter: Uncertainty-aware universal machine-learning potentials calibrated on experiments
Matthias Kellner, Teitur Hansen, Thomas Bligaard +2
Machine-learning models of atomic-scale interactions achieve the accuracy of the quantum mechanical calculations on which they are trained, but at a dramatically lower computationa…
Tracking the Lithiation State of LiSi from Machine-Learned XPS Binding Energies
Michael Alejandro Hernandez Bertran, Davide Tisi, Federico Grasselli +3
X-ray Photoelectron Spectroscopy (XPS) is a powerful technique to probe chemical states and interfacial processes in battery materials, but a quantitative interpretation is often h…
Learning Long-Range Representations with Equivariant Messages
Egor Rumiantsev, Marcel F. Langer, Tulga-Erdene Sodjargal +2
Machine learning interatomic potentials trained on first-principles reference data are becoming valuable tools for computational physics, biology, and chemistry. Equivariant messag…
Roadmap on Advancements of the FHI-aims Software Package
Joseph W. Abbott, Carlos Mera Acosta, Alaa Akkoush +203
Electronic-structure theory is the foundation of the description of materials including multiscale modeling of their properties and functions. Obviously, without sufficient accurac…
Natural Aging and Vacancy Trapping in Al-6xxx
Abhinav C. P. Jain, M. Ceriotti, W. A. Curtin
Undesirable natural aging (NA) in Al-6xxx delays subsequent artificial aging (AA) but the size, composition, and evolution of clustering are challenging to measure. Here, atomistic…