Fast Evaluation of the Adsorption Energy ofOrganic Molecules on Metals via GraphNeural Networks
Why this work is in the frame
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Bibliographic record
Abstract
Modeling of solid-state material-molecule interfaces in heterogeneous catalysis requires the extensive evaluation of the energy of molecules on surfaces. Obtaining the binding energy of many configurations of large organic molecules requires a vast amount of computational time with density functional theory (DFT). Here, we use a graph neural network (GNN) to evaluate the adsorption energy of molecular species adsorbed on metallic surfaces. The GNN is trained on a set of C1–4 fragments including N, O, S heteroatoms and C6–10 aromatic rings. Compared to DFT, the GNN shows a mean absolute error (MAE) of 0.17 eV on the test set being 6 orders of magnitude faster. When applying the trained model with subsequent hyperparameter optimization to molecules of industrial interest (biomass, plastics and polyurethanes precursors) containing up to 22 carbon atoms, the prediction performance for the adsorption energy yields a MAE of 0.03 eV/(non-H atom). While the error for out-of-distribution molecules is higher, it is still within the acceptable limit for adsorption energies (0.05 eV/atom), confirming the viability of the approach. The proposed framework represents a potential tool for the fast screening of catalytic materials, as well as their inverse design, enabling the multi-scale modeling for systems that cannot be easily simulated by DFT.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it