Run xtalnet_csp_predict from a SMILES string and sort candidates by energy_kcal, then screen polymorphs or match an experimental PXRD pattern.

You want to know what solid form(s) a compound is likely to adopt; this walks through predicting crystal structures from a SMILES string, screening for polymorphs, and matching against an experimental PXRD pattern.

Crystal structure prediction with XTalNet in .bloom*

Workflow 1: De novo crystal structure prediction from SMILES

Open XTalNet

Navigate to Tools > XTalNet in .bloom*, or ask Ivy:

Predict crystal structure forms for aspirin (CC(=O)Oc1ccccc1C(=O)O)

Configure the prediction

Parameter	Value
Tool	`xtalnet_csp_predict`
`compound_smiles`	`CC(=O)Oc1ccccc1C(=O)O`
`num_candidates`	`10` (default)
`analysis_level`	`standard` (default)

Review the candidates

The job returns, on completion:

structures_cif: CIF files for each candidate structure
energy_landscape_json: per-candidate energy_kcal, space_group, density, cell_parameters
summary_csv: a flat table of the same fields for quick sorting

Sort by energy_kcal — the lowest-energy candidates are the most thermodynamically favorable predictions.

Take a candidate forward

Feed a predicted structures_cif entry into PyWPEM's simulate_xrd to see what diffraction pattern that structure would produce, or compare it against an experimental pattern with OpXRD similarity search.

Workflow 2: Polymorph screening

Use this when you specifically want a stability-ranked set of polymorphs, or a co-crystal prediction.

Configure the screen

Parameter	Value
Tool	`xtalnet_polymorph_predict`
`compound_smiles`	Your compound's SMILES
`search_depth`	`standard` (default) — use `thorough` for late-stage candidates
`num_candidates`	`25` (default, range 10-50)
`temperature`	`298` K (default) — use `310` for body temperature

Or ask Ivy:

Run XtalNet polymorph screening on ritonavir and rank the forms by stability at body temperature

For a co-crystal prediction, add a coformer:

Predict co-crystals of this compound with nicotinamide

which sets coformer_smiles to nicotinamide's SMILES (NC(=O)c1cccnc1).

Review the ranking

The result's stability_ranking_json gives {forms: [{form_id, energy_kcal, stability_rank, is_metastable}], transition_risks: []}. The lowest stability_rank is the most stable form at the requested temperature; is_metastable: true flags forms at risk of converting to a more stable one over time.

Check formulation relevance

If you need solubility and bioavailability context, enable include_pharmaceutical_properties (default true for polymorph screening) and read pharmaceutical_properties_csv — [form_id, solubility, dissolution_rate, bioavailability_class] per form.

Workflow 3: Match an experimental PXRD pattern

If you already measured a diffraction pattern and want to identify which crystal form produced it, provide PXRD data instead of a SMILES string:

Parameter	Value
`pxrd_data`	`[[2theta, intensity], ...]` pairs from your measurement
`compound_formula`	Required when matching by PXRD (e.g. `"C9H8O4"`)
`pxrd_metadata.wavelength`	Default `1.5406` Å (Cu K-alpha) — set if your instrument differs

Match this experimental PXRD pattern against candidate crystal structures for aspirin (C9H8O4): [[5.0, 100], [10.5, 250], [15.2, 400], ...]

Next steps

See inputs & restraints for the full parameter set on both tools
See outputs & exports for artifact and metric definitions
Use PyWPEM to simulate the diffraction pattern for a predicted structure, or OpXRD to search for a matching experimental one

XTalNet Quickstart