- A. Naik2 · h 9
- Abril Azócar Guzmán1 · h 1
- C. Ertural1 · h 12
- Department of Materials Chemistry Bam Berlin Germany1 · h 0
- G.-M. Rignanese1 · h 10
- H. Luu1 · h 6
- Institute of Condensed Matter Theory1 · h 0
- Janine George1 · h 1
- Katharina Ueltzen1 · h 5
- L. Ghiringhelli1 · h 37
- Mariano Forti1 · h 1
- Nidal Dhamrait1 · h 2
3 papers
cond-mat.mtrl-sci2026
Machine-learning octet -type binary compounds across chemical space with domain knowledge of the interatomic bond
Rohan Kumar, Mariano D. Forti, Aakash A. Naik +2
The prediction of the structural stability of octet -type binary compounds is a classical materials informatics problem. The challenge is to capture the relative stability of 4…
cond-mat.mtrl-sci2026
A critical assessment of bonding descriptors for predicting materials properties
Aakash Ashok Naik, Nidal Dhamrait, Katharina Ueltzen +4
Most machine learning models for materials science rely on descriptors based on materials compositions and structures, even though the chemical bond has been proven to be a valuabl…
cond-mat.mtrl-sci2026
Towards knowledge-based workflows: a semantic approach to atomistic simulations for mechanical and thermodynamic properties
Abril Azocar Guzman, Hoang-Thien Luu, Sarath Menon +3
Mechanical and thermodynamic properties, including the influence of crystal defects, are critical for evaluating materials in engineering applications. Molecular dynamics simulatio…