3 papers
cond-mat.mtrl-sci2026
Unveiling the Core of Materials Properties via SISSO and Sensitivity Analysis
Lucas Foppa, Matthias Scheffler
Interpretable AI can reveal physical principles governing intricate materials properties by uncovering explicit relationships between physical parameters and target properties. The…
cond-mat.mtrl-sci2025
Materials Database from All-electron Hybrid Functional DFT Calculations
Akhil S. Nair, Lucas Foppa, Matthias Scheffler
Materials databases built from calculations based on density functional approximations play an important role in the discovery of materials with improved properties. Most databases…
cond-mat.mtrl-sci2024
Materials-Discovery Workflows Guided by Symbolic Regression: Identifying Acid-Stable Oxides for Electrocatalysis
Akhil S. Nair, Lucas Foppa, Matthias Scheffler
The efficiency of active learning (AL) approaches to identify materials with desired properties relies on the knowledge of a few parameters describing the property. However, these…