A Study of Two-Dimensional Single Atom-Supported MXenes as Hydrogen Evolution Reaction Catalysts Using DFT and Machine Learning
Bibliographic record
Abstract
<p></p><p>Enclosed you will find the article entitled “A study of two-dimensional single atom-supported MXenes as hydrogen evolution reaction catalysts using DFT and machine learning”.<br></p> <p> </p> <p>Existing studies predominantly focused on the hydrogen evolution reaction (HER) activities and stabilities of oxygen-terminated MXenes with single-atom loading. However, to the best of our knowledge, two-dimensional (2D) MXenes with different terminations (e.g. Br, I, Se, Te, B, Si, P, and NH) have not yet been investigated for the purposes of HER catalysis. Therefore, in this work, we considered the combined effect of the different surface terminations (B, NH, O, F, Si, P, S, Cl, Se, Br, Te, and I) and single atom loading (Ti, V, Fe, Co, Ni, Cu, Zr, Nb, Mo, Tc, Ru, Rh, Pd, Ag, Hf, Ta, W, Re, Os, Ir, Pt, and Au) using DFT calculation. Gibbs free energy of hydrogen adsorption (reflecting activity) and the cohesive energy (a proxy for thermal stability) of these structures (264 in total) were calculated. We demonstrate that 21 uninvestigated 2D single-atom MXene catalysts, among 264 promising candidates, show an electrocatalytic activity surpassing that of platinum and a thermal stability surpassing those of synthesized borophene sheet and MoS<sub>2</sub>. Moreover, all catalysts examined in this work were further randomly separated into training and test sets with a ratio of 7:3. The HER electrocatalytic performance and thermal stability of the catalysts in the test set were predicted by machine learning algorithms. Most importantly, we present a way to provide a comparable precision (root mean square error values for the activity and thermal stability predictions are 0.158 eV and 0.02 eV, respectively) to the published machine learning works by avoiding their adoption of complex electronic features and the associated high computational cost, and <i>by only using features that are </i><i>easily available in chemical repositories</i>. The algorithms used in this work are expected to help future researchers quickly screen single atom loaded MXenes HER catalysts at the initial design stage in a cost-effective manner. </p> <p> </p> <p>We have no financial interest in the subject or instrumentation used and there is no known conflict of interest. </p><br><p></p>
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".