34 citations · 43 across the 12 of their papers we have counts for
12 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…
A Critical Examination of Active Learning Workflows in Materials Science
Akhil S. Nair, Lucas Foppa
Active learning (AL) plays a critical role in materials science, enabling applications such as the construction of machine-learning interatomic potentials for atomistic simulations…
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…
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…
Hierarchical symbolic regression for identifying key physical parameters correlated with bulk properties of perovskites
Lucas Foppa, Thomas A. R. Purcell, Sergey V. Levchenko +2
Symbolic regression identifies key physical parameters describing materials properties by uncovering correlations as nonlinear analytical expressions. However, the pool of expressi…
Identifying outstanding transition-metal-alloy heterogeneous catalysts for the oxygen reduction and evolution reactions via subgroup discovery
Lucas Foppa, Luca M. Ghiringhelli
In order to estimate the reactivity of a large number of potentially complex heterogeneous catalysts while searching for novel and more efficient materials, physical as well as dat…
Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence
Lucas Foppa, Luca M. Ghiringhelli, Frank Girgsdies +10
Heterogeneous catalysis is an example of a complex materials function, governed by an intricate interplay of several processes, e.g., the different surface chemical reactions, and…