First-principles data for solid-solution Nb–Zr, Ta–Zr, and V–Zr binary alloys with body-centered-cubic structures
5 affiliations
- 1Computational Sciences and Engineering Division
- 2Oak Ridge National Laboratory
- 3Materials Sciences and Technology Division
- 4Computer Science and Mathematics Division
- 5National Center of Computational Sciences
Abstract
We present three open-access datasets containing density functional theory (DFT) calculations for refractory solid-solution binary alloys niobium–zirconium (NbZr), tantalum–zirconium (TaZr), and vanadium–zirconium (VZr) in 128-atom body-centered-cubic (BCC) supercells. These datasets extend our previously published first-principles collections for NbTa, NbV, TaV, and NbTaV alloys and were generated with the same automated high-throughput workflow based on the Vienna Ab-Initio Simulation Package (VASP). For each of the three NbZr, TaZr, and VZr systems, we uniformly sample 31 chemical compositions across the full composition range in 4-atom increments. For every composition we construct 100 distinct disordered atomic configurations by randomizing the element assignments on the BCC lattice sites. All structures are fully relaxed, and the released data include complete input files, detailed VASP outputs for each relaxation stage, and derived physical quantities such as ground-state formation energy and root mean-squared displacement (RMSD) relative to an ideal BCC reference. These Zr-containing binaries are motivated by our earlier demonstration that graph neural networks (GNNs) trained on binary NbTa, NbV, and TaV data can interpolate formation enthalpies and structural descriptors in ternary NbTaV alloys while dramatically reducing the required number of ternary DFT calculations. The new NbZr, TaZr, and VZr datasets increase the diversity of available binary training data, in particular in systems with larger size mismatch and stronger lattice distortion due to Zr, and are intended to support the development of transferable machine learning (ML) interatomic models for accelerated exploration of high-dimensional multicomponent alloy spaces.
Related
- High-Entropy Alloys as Materials for Solid-State Hydrogen Storage: From Fundamental Principles to Directed Design Strategies
- First-principles prediction of the thermoelectric properties of Sn(S0.2Se0.8) alloys
- First-Principles Investigation of Multimodal Toxic Gas Sensing in Carbon-Tuned hBN-Graphene Alloys: Chemiresistive, Work-Function, and Optical Responses
- High-Throughput Combinatorial Screening of Refractory Amorphous Alloys for Fusion Capillary Systems via a Calibrated First-Principles Continuum-Thermodynamic Model
- First-principles and cluster expansion investigation of Ti-V-Mn AB₂ alloys: stability, electronic structure, and mechanical properties.
BibTeX
@misc{pasini2026firstprinciples,
title = {{First-principles data for solid-solution Nb–Zr, Ta–Zr, and V–Zr binary alloys with body-centered-cubic structures}},
author = {Massimiliano Lupo Pasini and German Samolyuk and Jong Youl Choi and David Rogers and Ying Yang},
howpublished = {ChemRxiv},
year = {2026},
month = oct,
doi = {10.26434/chemrxiv.15004330/v2},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15004330/v2},
note = {CC BY},
}
RIS
TY - UNPB
TI - First-principles data for solid-solution Nb–Zr, Ta–Zr, and V–Zr binary alloys with body-centered-cubic structures
AU - Massimiliano Lupo Pasini
AU - German Samolyuk
AU - Jong Youl Choi
AU - David Rogers
AU - Ying Yang
PY - 2026
DA - 2026/10/09
PB - ChemRxiv
DO - 10.26434/chemrxiv.15004330/v2
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15004330/v2
AB - We present three open-access datasets containing density functional theory (DFT) calculations for refractory solid-solution binary alloys niobium–zirconium (NbZr), tantalum–zirconium (TaZr), and vanadium–zirconium (VZr) in 128-atom body-centered-cubic (BCC) supercells. These datasets extend our previously published first-principles collections for NbTa, NbV, TaV, and NbTaV alloys and were generated with the same automated high-throughput workflow based on the Vienna Ab-Initio Simulation Package (VASP). For each of the three NbZr, TaZr, and VZr systems, we uniformly sample 31 chemical compositions across the full composition range in 4-atom increments. For every composition we construct 100 distinct disordered atomic configurations by randomizing the element assignments on the BCC lattice sites. All structures are fully relaxed, and the released data include complete input files, detailed VASP outputs for each relaxation stage, and derived physical quantities such as ground-state formation energy and root mean-squared displacement (RMSD) relative to an ideal BCC reference. These Zr-containing binaries are motivated by our earlier demonstration that graph neural networks (GNNs) trained on binary NbTa, NbV, and TaV data can interpolate formation enthalpies and structural descriptors in ternary NbTaV alloys while dramatically reducing the required number of ternary DFT calculations. The new NbZr, TaZr, and VZr datasets increase the diversity of available binary training data, in particular in systems with larger size mismatch and stronger lattice distortion due to Zr, and are intended to support the development of transferable machine learning (ML) interatomic models for accelerated exploration of high-dimensional multicomponent alloy spaces.
ER -