80 citations · 132 across the 9 of their papers we have counts for
8 papers · 1 filter
Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles
Mikołaj 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…
High-quality, high-information datasets for universal atomistic machine learning
Cesare Malosso, Filippo Bigi, Paolo Pegolo +7
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…
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…
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…
Efficient All-electron Hybrid Density Functionals for Atomistic Simulations Beyond 10,000 Atoms
Sebastian Kokott, Florian Merz, Yi Yao +6
Hybrid density functional approximations (DFAs) offer compelling accuracy for ab initio electronic-structure simulations of molecules, nanosystems, and bulk materials, addressing s…
Roadmap on Data-Centric Materials Science
Stefan Bauer, Peter Benner, Tristan Bereau +58
Science is and always has been based on data, but the terms "data-centric" and the "4th paradigm of" materials research indicate a radical change in how information is retrieved, h…