Heterogeneous graph neural network with Voronoi pore nodes for predicting geometric, gas adsorption, and thermal properties of metal-organic frameworks. Per-atom and per-pore XAI attributions with iRASPA CIF export.
Ready for analysis
Upload a MOF CIF file on the left, select the ensemble, and click Run Prediction.
Or load one of the example structures below the upload field.
| Property | Prediction (mean ± std) | CV | Scenario |
|---|
Ensemble agrees and XAI attributions align with the physical direction of the property.
Ensemble agrees on the value, but attributions do not align with physical expectation. Interpret with caution.
XAI attributions are consistent but ensemble variance is high. The model is uncertain despite coherent explanations.
High ensemble variance and incoherent attributions. Physical validation required before acting on this prediction.
The CIF file below encodes per-atom XAI attributions in the
_atom_site_B_iso_or_equiv column (B-factor field).
Values are scaled from 1 (most negative attribution) through 50 (neutral)
to 99 (most positive attribution).
File → Open….
Step 2 — Color by attribution
In the right-hand panel: Appearance → Atoms → Color by →
Temperature Factor. Select a blue-white-red colormap for clearest contrast.
Step 3 — Other viewers
The same B-factor column is read by Mercury, OVITO, ChimeraX, VESTA, and any
CIF viewer that supports B-factor coloring. In VESTA, use
Edit → Color Settings → Isosurface / B-factors.
Interpretation
Blue atoms drive the property toward lower values; red atoms drive it higher.
White atoms contribute near-zero. For properties like void fraction and surface
area, atoms with high positive attribution are adjacent to the pore interior
and are strong candidates for chemical modification.
PoreGCN ships three trained ensembles. Each is a 5-fold cross-validation top-k selection trained with multi-task learning and inverse-std loss weighting. Pick the ensemble whose training distribution most closely matches your CIF.
hMOF Gas — default, broadest target list Trained on 51,163 hypothetical MOFs from the Wilmer hMOF database. Predicts five geometric properties plus 14 gas adsorption capacities (CO2, N2, CH4, H2 across multiple pressures and temperatures) plus log10(CO2/N2) selectivity. Best for new MOFs whose target use case is gas separation, carbon capture, or hydrogen storage. Example MOF in this app: HKUST-1 (Cu paddlewheel, supports the CO2/N2 selectivity narrative). hMOF Geometric — lighter, geometry only Same training set as hMOF Gas (51,163 hypothetical MOFs) but predicts only the five geometric properties: void fraction (VF), gravimetric surface area (GSA), accessible surface area (ASA), largest cavity diameter (LCD), and pore-limiting diameter (PLD). Use this when you only need pore geometry for a screening campaign and want the smaller, faster model. Example MOF in this app: MOF-5 (canonical high-VF Zn IRMOF, the geometric benchmark in most MOF reviews). CoRE MOF — experimental structures, stability Trained on 2,737 EXPERIMENTAL MOFs from the CoRE MOF 2019 database. Predicts ASA, GSA, VF, LCD, PLD, thermal stability, and density. Use this when you have a synthesized MOF and care about thermodynamic descriptors that the hypothetical-MOF ensembles do not predict. The smaller training set means predictions are grounded in real experimental structures, but the chemical space coverage is narrower than the hMOF ensembles. Two example MOFs in this app are paired with this ensemble. UiO-66 is the canonical experimental benchmark for stability studies; it lands Scenario A on most CoRE MOF properties. Tb-MOF-CrystEngComm2023 is a CoRE MOF validation example selected from a dual-XAI-method search across all 2,737 training entries: it lands Scenario A on 5 of 7 properties under BOTH attribution methods (signed occlusion and gradient x input), with sub-5% relative error on each, while the remaining 2 properties (GSA, ASA) are correctly flagged Scenario C and the predictions are 20-41% off the ground-truth values. The example concretely demonstrates the trustworthiness framework as a working filter, regardless of which XAI method the live tool runs. Why hMOF Gas is the default It has the broadest target list and is trained on the largest dataset. For a new user uploading a CIF without a specific property in mind, the gas-adsorption ensemble returns the most useful per-prediction information. Switch to hMOF Geometric if your question is purely geometric and you want faster inference; switch to CoRE MOF if your structure is experimental and you need stability or density. A caution on the trustworthiness scenario The Trustworthiness tab classifies each prediction A/B/C/D based on ensemble agreement and XAI directional consistency. Only Scenario A predictions are recommended for downstream screening without further validation. The choice of ensemble does not change this classification, but a CIF that sits well outside an ensemble's training distribution is more likely to fall into Scenario C or D. XAI method — Fast vs Slow attribution The "XAI method" radio in the input panel selects how per-atom and per-pore attributions are computed.Suggestion: {suggestion}
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