8 citations · 8 across the 2 of their papers we have counts for
6 papers
Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles
MikoÅaj J. Gawkowski, Nongnuch Artrith, Silvia Bonfanti +14
Foundation machine learning interatomic potentials (MLIPs) are increasingly being used as drop-in replacements for first-principles calculations, enabling simulations of materials…
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…
High-quality, high-information datasets for universal atomistic machine learning
Cesare Malosso, Filippo Bigi, Paolo Pegolo +5
The quality, consistency, and information content of training data is often what determines the practical value of machine-learning models for atomistic simulations. Yet, many wide…
Electric-Field Driven Nuclear Dynamics of Liquids and Solids from a Multi-Valued Machine-Learned Dipolar Model
Elia Stocco, Christian Carbogno, Mariana Rossi
The driving of vibrational motion by external electric fields is a topic of continued interest, due to the possibility of assessing new or metastable material phases with desirable…
Density-Functional Perturbation Theory with Numeric Atom-Centered Orbitals
Connor L. Box, Reinhard J. Maurer, Honghui Shang +4
This paper represents one contribution to a larger Roadmap article reviewing the current status of the FHI-aims code. In this contribution, the implementation of density-functional…
Polarisation, Born Effective Charges, and Topological Invariants via a Berry-Phase Approach
Christian Carbogno, Nikita Rybin, Sara Panahian Jand +4
This paper represents one contribution to a larger Roadmap article reviewing the current status of the FHI-aims code. In this contribution, the implementation of polarization, Born…