1 citations · 1 across the 6 of their papers we have counts for
8 papers
An efficient approach to electron-phonon coupling calculations of heterostructures within an all-electron framework
Ignacio Gonzalez Oliva, Sebastian Tillack, Fabio Caruso +2
A major challenge in calculating electron-phonon coupling at interfaces from first principles is the prohibitive computational cost due to the large supercells involved. Here, we p…
FUCrIMODo: structure recovery from atomistic descriptors via multi-stage genetic algorithms
Louis Boehm, Martin Kuban, Claudia Draxl
Data-driven approaches to materials discovery rely on numerical representations of atomic structures as input for machine learning models. Inverting these descriptors - recovering…
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Aritra Roy, Kevin Shen, Andrew MacBride +350
Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broa…
An exciting approach to theoretical spectroscopy
Martí Raya-Moreno, Alexander Buccheri, Noah Alexy Dasch +27
Theoretical spectroscopy, and more generally, electronic-structure theory, are powerful concepts for describing the complex many-body interactions in materials. They comprise a var…
Impact of electron-phonon interaction on the electronic structure of interfaces between organic molecules and a MoS monolayer
Ignacio Gonzalez Oliva, Sebastian Tillack, Fabio Caruso +2
By means of first-principles calculations, we investigate the role of electron-phonon interaction in the electronic structure of hybrid interfaces, formed by MoS and monolayers…
Automatic Generation of Maximally Localized Wannier Functions via Optimized Projection Functions and Self-projections
Sebastian Tillack, Claudia Draxl
We present an automatized approach towards maximally localized Wannier functions (MLWFs) applicable to both occupied and unoccupied states. We overcome limitations of the standard…
Training speedups via batching for geometric learning: an analysis of static and dynamic algorithms
Daniel T. Speckhard, Tim Bechtel, Sebastian Kehl +2
Graph neural networks (GNN) have shown promising results for several domains such as materials science, chemistry, and the social sciences. GNN models often contain millions of par…
How big is Big Data?
Daniel T. Speckhard, Tim Bechtel, Luca M. Ghiringhelli +3
Big data has ushered in a new wave of predictive power using machine learning models. In this work, we assess what {\it big} means in the context of typical materials-science machi…