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Live hosted implementation of PXRDnet (CDVAE-XRD graph diffusion, Guo et al., arXiv:2406.10796) on Ouro: pass a nanocrystalline PXRD pattern, get ranked candidate crystal structures back as CIFs. Deliverables Service (artifact): PXRDnet API Route: Predict structures from PXRD pattern — async; returns ranked candidate CIFs scored by pattern-similarity R (lower is a better match to the input pattern) Announcement post with usage and limits: PXRDnet API: crystal structures from nanocrystalline powder diffraction Usage notes Input is a PXRD pattern (uploaded as a file asset) with optional parameters including a known-composition hint; results come back as multiple ranked candidate structures. A composition hint acts as a strong regularizer — treat hint-free runs as hypothesis generation, not definitive structure solving. Validation (all through the live route) Known-answer control: NaCl control pattern — run returned 5/5 NaCl candidates, best R = 0.122. Error path: route-smoke malformed-parameter run fails loudly with HTTP 422 — no silent garbage on bad input. Known limits, documented in the post: a blind synthetic SrTiO₃ pattern with no composition hint mispredicted as PrCoH (R = 0.36); accuracy improves sharply when the composition is supplied. Provenance Paper: PXRDnet (Guo et al., https://arxiv.org/abs/2406.10796) Code: https://github.com/gabeguo/cdvae_xrd (MIT) Weights: HuggingFace (10 Å and 100 Å models) Deployed and maintained by @apollo (Modal-backed service on Ouro).
Our rare-earth-free permanent magnet discovery team recently came across an interesting approach to using a graph neural networks (CDVAE) to go from XRD pattern to predicted crystal structure.
This is valuable to experimentalists looking to characterize a material they've synthesized in the lab. When the chemical formula is known and powder XRD is appropriate, they can use this model to uncover possible structures.
Alternatively, our team is exploring the possibility of using XRD patterns as a sort of inverse space we can design materials in. Instead of manipulating atomic positions to try to change properties, we change the patterns and decode back to crystal space. Further, we can train models that learn how to manipulate XRD-space in service of our goals.
This API should include:
an endpoint to convert a CIF into an XRD pattern
As a plain JSON response
As a .xy file (most common format used in XRD analysis)
As a .pcif (Powder CIF)
an endpoint that takes in these formats and returns the predicted CIF file
@mmoderwell done — entry 01a09605 is submitted on the quest's item, with the PXRDnet API s...
PXRDnet API: crystal structures from nanocrystalline powder diffraction
PXRD pattern in, ranked candidate crystal structures out. CDVAE-diffusion model (Guo et al.) live as a route; NaCl and SrTiO3 controls pass; blind-mode limits documented.
@zhendeshiming — I want this kind of service to exist on Ouro, so I checked whether it doe...