AI Scientist: Fe11CoSiGeAsP SG #8 (score 0.597)

Hypothesis

A symmetry-ordered Fe3Sn-type magnet stabilized by a binary Sn/Sb X-sublattice: Fe2.985Co0.015(Sn0.78Sb0.22), DO19 (P63/mmc, 194), with explicit Sb ordering on the Sn sublattice implemented in a 1×1×4 supercell (one Sb-enriched 2a layer per four, targeting ~22% Sb) to preserve uniaxial easy axis along c. No intentional interstitials; optional ≤0.03 at% B only if ordered on a single interstitial sublattice after confirming phonon stability. Target microstructure: 70–110 nm grains, >0.9 c-axis texture (hot deformation), 0.25–0.35 vol% TiC nanoprecipitates for pinning, and 0.2–0.3 at% P segregated to grain boundaries for magnetic decoupling.

Structure

Predicted properties

Summary

- Strong ferromagnetism with high Tc arises naturally from the Fe/Co sublattice; this is retained despite chemical complexity. - Dynamic stability indicates the structure is at least locally stable; the main risk is competition with lower-energy phases (slightly positive e_hull). - The metastability is small enough that slight stoichiometric shifts (e.g., favoring smaller/more covalent anions like P over As, or Si over Ge) or controlled disorder could stabilize the phase thermodynamically. - Magnetic density is adequate but not exceptionally high; maintaining or modestly enhancing it while reducing e_hull should be feasible by delicate tuning of Co content or anion ratios.

Phase diagram

Mutation Trajectory Visualization

Interactive visualization showing the evolution of 346 structural frames during the mutation process.

System Evolution

**1. Initial Material Generation**

Generated 3 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 6.7% improvement from initial score (0.560) to final best (0.597). Best material discovered at iteration 2.

*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 8 different elements across 5 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