1 citations · 1 across the 7 of their papers we have counts for
7 papers
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…
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…
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…
Coherent Collections of Rules Describing Exceptional Materials Identified with a Multi-Objective Optimization of Subgroups
Lucas Foppa, Matthias Scheffler
Useful materials are often statistically exceptional and they might be overlooked by AI models that attempt to describe all materials simultaneously. These global models perform we…
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…
From Prediction to Action: Critical Role of Performance Estimation for Machine-Learning-Driven Materials Discovery
Mario Boley, Felix Luong, Simon Teshuva +3
Materials discovery driven by statistical property models is an iterative decision process, during which an initial data collection is extended with new data proposed by a model-in…
Towards a Multi-Objective Optimization of Subgroups for the Discovery of Materials with Exceptional Performance
Lucas Foppa, Matthias Scheffler
Artificial intelligence (AI) can accelerate the design of materials by identifying correlations and complex patterns in data. However, AI methods commonly attempt to describe the e…