Redox-Driven Electronic Structure Collapse in Fe₄N₂ Clusters: Discovery of Single-Reference Pathways for Nitrogen Activation via 20-Qubit VQE on Consumer GPU Hardware
Abstract
# Redox-Driven Electronic Structure Collapse in Fe₄N₂ Clusters## OverviewThis dataset presents the **discovery of "redox-driven electronic structure collapse"** in biomimetic Fe₄N₂ butterfly clusters—a phenomenon where electron transfer fundamentally transforms quantum mechanical complexity. Through systematic Variational Quantum Eigensolver (VQE) calculations spanning three oxidation states (cation, neutral, anion) and complete N–N bond dissociation coordinates (1.1–2.0 Å), we demonstrate that **reduction eliminates multi-reference correlation**, creating what we term a **"single-reference highway"** for nitrogen activation.---## Relationship to Previous WorkThis work represents **Phase 3** of our systematic VQE investigation of nitrogen fixation mechanisms. Together, these three deposits establish a complete computational framework from validation to discovery:### Phase 1: Validation & Benchmarking**DOI:** [10.5281/zenodo.18356899](https://doi.org/10.5281/zenodo.18356899) **Title:** Quantum-Classical Hybrid VQE Benchmarks for Open-Shell Transition-Metal Chemistry: Nitrogen Fixation Intermediates (Chatt Cycle) **Status:** Under review, *quant-ph* **Achievement:** Established VQE benchmarks for Chatt cycle intermediates, validating chemical accuracy for open-shell transition metal systems.### Phase 2: Scaling & Feasibility**DOI:** [10.5281/zenodo.18382689](https://doi.org/10.5281/zenodo.18382689) **Title:** Chemical Accuracy for High-Spin Tri-Iron Nitrogen Activation: Computational Feasibility of Multi-Metal VQE at 18 Qubits **Achievement:** Demonstrated computational feasibility of multi-metal cooperativity at 18 qubits, achieving
Fields of Study & Subjects
Advanced Chemical Physics Studies
Quantum Computing Algorithms and Architecture
Metalloenzymes and iron-sulfur proteins
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 30 January 2026
- Language
- EN
- Document type
- Dataset