Automated Exploration of Heterogeneous Catalysis with a Gas–Solid Nanoreactor
Bibliographic record
Abstract
We present an automated method, gas–solid nanoreactor molecular dynamics (GS-NMD), designed to explore reaction space and construct reaction networks for complex gas–solid heterogeneous catalysis systems by integrating multiple acceleration techniques. Periodic pulses were used to drive gas-phase molecules toward the catalyst surface, accelerating adsorption and Eley–Rideal reactions. Adsorbed species were then subjected to metadynamics to overcome reaction barriers associated with migration, Langmuir–Hinshelwood-type reactions, and desorption, using the root-mean-square deviations in Cartesian space as collective variables. We demonstrate the efficiency of GS-NMD with the case of N 2 dissociation on Fe surfaces, showing its ability to effectively screen for low-barrier reactions within a vast reaction space and distinct catalysts of different performances. Additionally, we illustrate the method’s utility in constructing effective reaction networks for heterogeneous catalysis, exemplified by ammonia synthesis, which comprises only low-barrier elementary steps. These results suggest that GS-NMD is a promising and efficient tool for the automated exploration of heterogeneous catalysis, enabling the identification of the most favorable mechanisms and active sites for gas–solid reactions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".