AI Scientist: Fe4Mn3B4 SG #1 (score 0.728)

Hypothesis

A ternary Fe-Mn-B alloy with a body-centered tetragonal or orthorhombic crystal structure, where boron occupies interstitial sites, will exhibit high Curie temperature (>500 K), magnetic density >0.1 (μB/atom), moderate complexity (≤20 atoms/unit cell), and low formation energy (e_hull ≤ 0.15 eV/atom), while achieving enhanced magnetic anisotropy energy sufficient for permanent magnet applications.

Structure

Predicted properties

Summary

The Fe4Mn3B4 compound shows promising magnetic ordering temperature and dynamic stability, suggesting good intrinsic magnetic behavior and structural robustness. The main challenge is its thermodynamic stability, as indicated by the high energy above hull. The magnetic density is close but slightly below the target, suggesting that minor compositional or structural modifications might improve it. The low cost and atom count within limits make it a practical candidate if stability can be enhanced.

Phase diagram

System Evolution

**1. Initial Material Generation**

Generated 2 initial material candidates using AI-driven hypothesis generation. Started with 1 from-scratch generations.

*Reasoning:* System begins with broad exploration to establish baseline materials and understand the chemical space, building up a database of candidates for future mutation operations.

**2. Performance Optimization Convergence**

Achieved 0.0% improvement from initial score (0.728) to final best (0.728). Best material discovered at iteration 0.

*Reasoning:* The evolutionary process successfully optimized target properties through iterative refinement, with the AI learning to generate progressively better materials by leveraging successful mutation patterns and chemical insights.

**3. Chemical Space Diversification**

Explored 4 different elements across 2 unique compositions, systematically mapping the rare-earth-free magnetic material space.

*Reasoning:* Comprehensive exploration of chemical diversity ensures the discovery process doesn't get trapped in local optima and identifies the most promising regions of chemical space for permanent magnet applications.

Iterations