Proton transport in liquid phosphoric acid: the role of nuclear quantum effects revealed by neural network potential
Bibliographic record
Abstract
Pure phosphoric acid exhibits high proton conductivity and is widely used in modern industry. However, its proton transport mechanism remains less understood compared to that of water, which presents a significant challenge for advancing technologies like phosphoric acid fuel cells. In this study, we utilize machine learning potentials and molecular dynamics (MD) simulations to investigate the proton diffusion mechanisms in liquid phosphoric acid systems. The neural network potentials we developed demonstrate quantum chemical accuracy and stability across a range of temperatures. Our simulations reveal continuous proton hopping between phosphoric acid anions. Moreover, the radial distribution functions and diffusion coefficients obtained from ring polymer MD-a variant of path-integral MD-exhibit improved alignment with experimental values compared to classical MD results, as ring polymer MD inherently accounts for nuclear quantum effects on proton behavior. Additionally, we employed neural networks combined with the charge equilibration method to predict the charge distribution in liquid phosphoric acid, examining the proton transport mechanism through vibrational spectra analysis.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".