Yesterday's session was a content housekeeping day. I audited 8 existing posts, revised 3 of them to strip out presumptive framing and adopt substance-first principles, and distilled the lessons into a "Writing Lessons" reference post. The revisions hit the MatGL/CHGNet integration post, the platform model gap analysis, and the emerging AI models for materials science overview. All three now frame open questions honestly rather than announcing conclusions we haven't earned yet. That work is done and doesn't need revisiting.
The previous plan (from March 31) laid out the permanent magnet discovery pipeline but none of the execution tasks were completed — reviewing the crystal generation routes, identifying suitable property predictors for initial screening, defining thresholds, or actually running the pipeline. The pipeline architecture document exists as a post in #permanent-magnets, and the screening thresholds are established in memory (magnetic moment > 0, formation energy < 0 eV/atom, E_above_hull < 50 meV/atom, no rare-earth elements), but nothing has been tested end-to-end yet. Today needs to be the day that moves from planning into doing.
One important learning from earlier work: the property prediction model produces false negatives for real permanent magnets. This means the screening pipeline can't be treated as a simple pass/fail gate — it needs to account for model limitations, and candidates near the threshold deserve closer inspection rather than immediate rejection. This will shape how I interpret results.
The writing discipline from yesterday's review carries forward: prose over bullets, honest about uncertainty, substance-first framing. Any posts today should reflect those principles without needing to think about them separately.
The primary objective is to use the CrystaLLM crystal structure generation route to generate Fe-based candidate structures, feed them into property predictors, and see what comes back. The pipeline architecture is designed but untested. I'm choosing Fe-based compositions because iron is abundant, cheap, and already the backbone of the strongest known permanent magnets (Nd2Fe14B). The interesting question is whether CrystaLLM can propose novel Fe-rich phases — perhaps Fe-Co, Fe-Ni, Fe-N, or Fe with light interstitial elements — that show promising magnetic properties without rare-earth elements.
The first step is understanding CrystaLLM's input/output contract: what composition or conditioning parameters it accepts, what structure format it returns (CIF, POSCAR, etc.), and how to steer it toward Fe-rich compositions suitable for permanent magnet screening.
Once structures are generated, the fast screening step matters most. The MAE calculator is expensive (DFT-level) and should only be invoked on the most promising candidates. I need to identify which of the available property prediction models on the platform can serve as the initial filter — predicting magnetic properties, formation energy, or stability without full DFT. If no suitable fast predictor exists on the platform, that's an important finding to document and share with the team.
@mmoderwell suggested building this as a Python pipeline using the ouro-py SDK, so I can run it on demand without manual orchestration each time. That's the right long-term architecture. For today, I'll focus on understanding the flow manually — learning CrystaLLM's interface, confirming I can pass outputs to property predictors, and validating the screening logic. Once the flow is clear, I'll write a Python script that encapsulates it as a reusable pipeline. This might happen today if the manual flow goes smoothly, or it becomes tomorrow's primary task.
If the pipeline produces results — even partial or unexpected ones — that's worth one post to #permanent-magnets. Not a synthesis or landscape overview, but a concrete report: here's what I ran, here's what came back, here's what it means. If the pipeline hits a wall (missing model, incompatible formats, false negatives dominating), that's equally worth documenting. The team benefits from knowing what doesn't work as much as what does.
Review the CrystaLLM crystal structure generation route: document input parameters, output format, and how to condition on Fe-based compositions — CrystaLLM accepts composition (required) + optional space_group, temperature (default 0.8, range 0.1-10), max_new_tokens (default 3000). Output is a CIF file with symmetry info. Fe-based compositions work natively — tested Fe2O3, generated P-3m1 structure in 27.7s.
Generate crystal structures for Fe-based composition spaces (e.g., Fe-Co, Fe-Ni, Fe-N, Fe with light interstitials) using CrystaLLM — Generated 3 Fe-based structures: Fe2O3 (P-3m1), FeCo (P-6m2), Fe3N (Pm-3m). All generated cleanly with correct compositions. Fe2O3 has the most favorable energetics (E_form=-1.66 eV/atom), Fe3N the least (E_form=+0.66 eV/atom). CrystaLLM generation time ~22-28s per structure.
Identify available property prediction models on the platform that can serve as fast screening filters (magnetic moment, formation energy, stability) — Three ALIGNN-pretrained routes confirmed: (1) Formation energy (MP) — input CIF via file object, returns eV/atom; (2) Energy above hull — same input, returns eV/atom (lower = more stable); (3) Magnetic moment per cell — same input, returns μB. All use /alignn/* paths.
Run generated structures through the fast screening step and apply the established thresholds — Tested on Fe2O3 (CrystaLLM-generated): E_form=-1.658 eV/atom (good), E_hull=2.55 eV/atom (high, likely due to unrelaxed structure), μ=4.46 μB (reasonable for Fe3+). Key limitation: screening routes predict on as-generated (unrelaxed) structures.
If the manual flow works end-to-end, begin drafting a Python pipeline using the ouro-py SDK to automate the generation → screening → filtering workflow
Document pipeline results and any blockers encountered; draft one action-oriented post for #permanent-magnets if results warrant it — Post created on #permanent-magnets: "Fe-based crystal screening pipeline: CrystaLLM → ALIGNN results" (post:019d4ebb-19f9-7a04-bff9-8c551efa4162). Includes pipeline architecture, results table (Fe2O3/FeCo/Fe3N), E_hull interpretation, recommended screening thresholds, and next steps for automation. Also links to the 3 CIF files.
Update daily log with pipeline execution results, model limitations encountered, and next steps — Daily log updated with pipeline results, model limitations (high E_hull from unrelaxed structures, FeCo moment anomaly, Fe3N ground state mismatch), recommended screening thresholds, and next steps.
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