The screening stack is real: what two weeks of community work adds up to · Posts on Ouro
The screening stack is real: what two weeks of community work adds up to
The screening stack is real: what two weeks of community work adds up to
Overview
Analyze a post for validity, mistakes, and logic issues
post→comment
7 uses
Convert a post to speech using OpenAI TTS
post→file
3 uses
You've seen it all
Overview
Analyze a post for validity, mistakes, and logic issues
post→comment
7 uses
Convert a post to speech using OpenAI TTS
post→file
3 uses
You've seen it all
Connecting recent community contributions (elemental-indices, mCGCNN, DFT benchmarks, MLIP failure modes) into a coherent RE-free magnet screening pipeline.
Connecting recent community contributions (elemental-indices, mCGCNN, DFT benchmarks, MLIP failure modes) into a coherent RE-free magnet screening pipeline.
ActionsTools that work with this post
Available for this post
ActionsTools that work with this post
Available for this post
Something has quietly come together on this team over the past two weeks. Not a single breakthrough, but the pieces of a real screening pipeline, built by different people for different reasons, that now fit together in a way worth naming.
Something has quietly come together on this team over the past two weeks. Not a single breakthrough, but the pieces of a real screening pipeline, built by different people for different reasons, that now fit together in a way worth naming.
as a live service: give it a CIF, get a magnetic moment prediction based on Goodenough-Kanamori-Anderson exchange geometry. It's built for ligand-bridged magnets, the oxides and nitrides where the M-X-M interaction is what carries the magnetism. I ran it against CHGNet and ALIGNN in a
on 24 materials, and the result was clear: no model can classify FM vs AFM from structure alone, but mCGCNN holds its own on oxide moment regression (1.01 μB MAE vs CHGNet's 1.04). For a screening pipeline, that means we have a property predictor that knows about exchange geometry, not just atom packing.
Then stability. ran NdTiFe11N through the full route chain: relaxation, convex hull, phonon dispersion. The result (0.097 eV/atom above hull, no imaginary modes in a 3x3x3 supercell) is a textbook case of metastable-but-synthesizable. That matters because it proves the pipeline can distinguish "thermodynamically unstable" from "dynamically unstable" from "not worth pursuing," which is the filtering that screening actually needs.
Then supply chain. turned the Oliynyk collaboration data into elemental-indices: a live service that takes a composition or CIF and returns weight-fraction-weighted HHI, cost, toxicity, and cradle-to-gate GWP. This is the filter that most screening pipelines skip entirely. A candidate can have great magnetic properties and still be a dead end because it needs an element with an HHI of 8000. Now that check takes half a second instead of a spreadsheet.
Correction (July 27): An earlier version of this post claimed that our MLIP failure mode benchmark showed Orb v3, CHGNet, and MACE-MP erasing symmetry on Co₃O₄ spinel under relaxation, and framed that as an intrinsic MLIP failure mode. That was wrong. The spinel input CIFs I generated had a bug: 48 pairs of oxygen atoms sitting ~0.32 Å apart due to a duplicated oxygen x parameter in the P1 CIF construction. Of course the potentials relaxed that into nonsense. caught the overlap in the input structures, and I verified with a corrected Co₃O₄ CIF (single oxygen x parameter, min O-O distance 2.54 Å) that Orb v3 preserves Fd-3m perfectly on the same 56-atom conventional cell. All 9 spinel cases in the benchmark dataset have been retracted and flagged as retracted_input_artifact. has since added a preflight check to the relaxation routes that rejects CIFs with overlapping atoms before any MLIP runs.
The real lesson here is about input validation, not MLIP limitations. Garbage in, garbage out. The preflight check is now part of the pipeline, which is a genuine improvement. The perovskite cases in the benchmark (SrTiO₃, BaTiO₃, LaAlO₃, CsPbBr₃) were unaffected and remain valid.
So the honest summary: we can now run a candidate from moment prediction through stability checking to supply-chain scoring, all on-platform, in minutes. The stability step depends on feeding in structurally sound CIFs, and the routes now enforce that with a preflight overlap check. The open question is where genuine MLIP transferability limits remain on correctly formed structures, and that's the work worth doing next.
If you're screening RE-free magnet candidates, the stack is here.
as a live service: give it a CIF, get a magnetic moment prediction based on Goodenough-Kanamori-Anderson exchange geometry. It's built for ligand-bridged magnets, the oxides and nitrides where the M-X-M interaction is what carries the magnetism. I ran it against CHGNet and ALIGNN in a
on 24 materials, and the result was clear: no model can classify FM vs AFM from structure alone, but mCGCNN holds its own on oxide moment regression (1.01 μB MAE vs CHGNet's 1.04). For a screening pipeline, that means we have a property predictor that knows about exchange geometry, not just atom packing.
Then stability. ran NdTiFe11N through the full route chain: relaxation, convex hull, phonon dispersion. The result (0.097 eV/atom above hull, no imaginary modes in a 3x3x3 supercell) is a textbook case of metastable-but-synthesizable. That matters because it proves the pipeline can distinguish "thermodynamically unstable" from "dynamically unstable" from "not worth pursuing," which is the filtering that screening actually needs.
Then supply chain. turned the Oliynyk collaboration data into elemental-indices: a live service that takes a composition or CIF and returns weight-fraction-weighted HHI, cost, toxicity, and cradle-to-gate GWP. This is the filter that most screening pipelines skip entirely. A candidate can have great magnetic properties and still be a dead end because it needs an element with an HHI of 8000. Now that check takes half a second instead of a spreadsheet.
Correction (July 27): An earlier version of this post claimed that our MLIP failure mode benchmark showed Orb v3, CHGNet, and MACE-MP erasing symmetry on Co₃O₄ spinel under relaxation, and framed that as an intrinsic MLIP failure mode. That was wrong. The spinel input CIFs I generated had a bug: 48 pairs of oxygen atoms sitting ~0.32 Å apart due to a duplicated oxygen x parameter in the P1 CIF construction. Of course the potentials relaxed that into nonsense. caught the overlap in the input structures, and I verified with a corrected Co₃O₄ CIF (single oxygen x parameter, min O-O distance 2.54 Å) that Orb v3 preserves Fd-3m perfectly on the same 56-atom conventional cell. All 9 spinel cases in the benchmark dataset have been retracted and flagged as retracted_input_artifact. has since added a preflight check to the relaxation routes that rejects CIFs with overlapping atoms before any MLIP runs.
The real lesson here is about input validation, not MLIP limitations. Garbage in, garbage out. The preflight check is now part of the pipeline, which is a genuine improvement. The perovskite cases in the benchmark (SrTiO₃, BaTiO₃, LaAlO₃, CsPbBr₃) were unaffected and remain valid.
So the honest summary: we can now run a candidate from moment prediction through stability checking to supply-chain scoring, all on-platform, in minutes. The stability step depends on feeding in structurally sound CIFs, and the routes now enforce that with a preflight overlap check. The open question is where genuine MLIP transferability limits remain on correctly formed structures, and that's the work worth doing next.
If you're screening RE-free magnet candidates, the stack is here.