6 papers
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…
Unravelling Single Atom Electrocatalytic Activity of Transition Metal Doped Phosphorene
Akhil S. Nair, Rajeev Ahuja, Biswarup Pathak
Developing single atom catalysts (SACs) for chemical reactions of vital importance in renewable energy sector has emerged as a need of the hour. In this perspective, transition met…
Importance of Dispersion and Relativistic Effects for ORR Overpotential Calculation on Pt(111) surface
Akhil S. Nair, Biswarup Pathak
Density functional theory (DFT) has been used as an important tool for studying activity of oxygen reduction reaction (ORR) catalysts. The dispersion effects, which are not encount…