Publications of Luca M. Ghiringhelli
All genres
Journal Article (62)
2025
Journal Article
Speckhard, Daniel, Christian Carbogno, Luca M. Ghiringhelli, , Matthias Scheffler and : Extrapolation to the complete basis-set limit in density-functional theory using statistical learning.
2024
Journal Article
Christian Carbogno, , , , Ralph Ernstorfer, , Lucas Foppa, , , , Luca M. Ghiringhelli, , , , , , , , , Sebastian Kokott, , , , Andreas Leitherer, , , , , , , , , , , Thomas Purcell, , , , , , , , , , , , , , Annette Trunschke, , , , , , and Matthias Scheffler: , , , , , Roadmap on data-centric materials science.
2023
Journal Article
Leitherer, Andreas, , and Luca M. Ghiringhelli: Automatic identification of crystal structures and interfaces via artificial-intelligence-based electron microscopy.
Journal Article
Purcell, Thomas, Matthias Scheffler and Luca M. Ghiringhelli: Recent advances in the SISSO method and their implementation in the SISSO++ Code.
Journal Article
Purcell, Thomas, Matthias Scheffler, Luca M. Ghiringhelli and Christian Carbogno: Accelerating materials-space exploration for thermal insulators by mapping materials properties via artificial intelligence.
2022
Journal Article
Carbogno, Christian, , Björn Bieniek, Claudia Draxl, Luca M. Ghiringhelli, , , , , , , and Matthias Scheffler: Numerical quality control for DFT-based materials databases.
Journal Article
Foppa, Lucas and Luca M. Ghiringhelli: Identifying Outstanding Transition‑Metal‑Alloy Heterogeneous Catalysts for the Oxygen Reduction and Evolution Reactions via Subgroup Discovery.
Journal Article
Foppa, Lucas, Thomas Purcell, , Matthias Scheffler and Luca M. Ghiringhelli: Hierarchical Symbolic Regression for Identifying Key Physical Parameters Correlated with Bulk Properties of Perovskites.
Journal Article
Foppa, Lucas, Christopher A. Sutton, Luca M. Ghiringhelli, , , , and Matthias Scheffler: Learning Design Rules for Selective Oxidation Catalysts from High-Throughput Experimentation and Artificial Intelligence.
Journal Article
Matthias Scheffler, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , and Luca M. Ghiringhelli: , , , , , , Roadmap on Machine learning in electronic structure.
Journal Article
Luca M. Ghiringhelli and : , , , , , , Finding predictive models for singlet fission by machine learning.
Journal Article
Mazheika, Aliaksei, Yanggang Wang, , , , Luca M. Ghiringhelli, and Matthias Scheffler: Artificial-intelligence-driven discovery of catalyst genes with application to CO2 activation on semiconductor oxides.
Journal Article
Purcell, Thomas, Matthias Scheffler, Christian Carbogno and Luca M. Ghiringhelli: SISSO++: A C++ Implementation of the Sure-Independence Screening and Sparisifying Operator Approach.
Journal Article
Regler, Benjamin, Matthias Scheffler and Luca M. Ghiringhelli: TCMI: a non-parametric mutual-dependence estimator for multivariate continuous distributions.
Journal Article
Luca M. Ghiringhelli and Matthias Scheffler: , , The NOMAD Artificial-Intelligence Toolkit: Turning materials-science data into knowledge and understanding.
Journal Article
Zhou, Yuanyuan, Chunye Zhu, Matthias Scheffler and Luca M. Ghiringhelli: Ab Initio Approach for Thermodynamic Surface Phases with Full Consideration of Anharmonic Effects: The Example of Hydrogen at Si(100).
2021
Journal Article
Luca M. Ghiringhelli and : , , Data-driven equation for drug-membrane permeability across drugs and membranes.
Journal Article
Luca M. Ghiringhelli and : , , Publisher's Note: "Data-driven equation for drug-membrane permeability across drugs and membranes" [J. J. chem. Phys. 154, 244114 (2021)].
Journal Article
Foppa, Lucas, Luca M. Ghiringhelli, Frank Girgsdies, Maike Hashagen, Pierre Kube, , Spencer Carey, Andrey Tarasov, Peter Kraus, , Robert Schlögl, Annette Trunschke and Matthias Scheffler: Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence.
Journal Article
Leitherer, Andreas, Angelo Ziletti and Luca M. Ghiringhelli: Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning.