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REVIEW 3 major objections 6 minor 165 references

Deep learning of committor and explainable artificial intelligence analysis for identifying reaction coordinates

T0 review · 3 major / 6 minor · reviewed 2026-08-02 · deepseek-v4-flash

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Pith's one-line read A deep neural network trained on committor values, read with explainable-AI attribution, reveals the few collective variables that actually govern molecular transitions — and where the transition-state dividing surface sits.

desk verdict A competent review of the authors' own committor+XAI framework, but the NaCl demonstration undercuts the central claim of 'well-defined boundaries' and the paper's own text admits it. read the letter →

arxiv 2603.25237 v2 pith:IJRSNTLQ submitted 2026-03-26 physics.chem-ph cond-mat.soft

Toshifumi Mori , Kei-ichi Okazaki , Kang Kim , Nobuyuki Matubayasi This is my paper · ORCID
classification physics.chem-phcond-mat.soft
keywords reactioncoordinatecommittordeeplearningexplainableAISHAPLIMEatom-centeredsymmetryfunctionsmoleculardynamics
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This review argues that the reaction coordinate for a molecular transition can be learned from committor probabilities without relying on physical intuition: a deep neural network maps candidate collective variables to a reaction coordinate, and explainable-AI tools (LIME, SHAP) then rank which input variables control the prediction. Applied to alanine dipeptide isomerization, the framework identifies dihedral θ as the dominant coordinate, with its importance peaking near the transition state in a way global linear models miss. Applied to NaCl dissociation in water, it identifies two atom-centered symmetry functions that, together with the interionic distance, separate associated from dissociated states by a well-defined p_B = 0.5 boundary. The review also shows that different neural-network architectures give nearly identical reaction coordinates and feature rankings, so the extracted mechanism does not depend on the specific model chosen. If right, the framework offers a data-driven path from simulation trajectories to interpretable molecular mechanisms in complex systems.

What carries the argument

The central object is the committor p*_B(R) — the probability of reaching state B before A with thermal velocities; the transition state is the p_B=1/2 surface. The machinery: sample near the saddle, estimate p*_B from short trajectories; train a multilayer perceptron mapping candidate collective variables to a reaction coordinate q, with cross-entropy loss enforcing p_B(q)=(1+tanh q)/2; then use LIME and SHAP to rank each input's contribution. This turns a black-box committor fitter into a mechanistic tool: the top-ranked CVs define low-dimensional PMFs whose isocommittor lines mark the TS.

What would settle it

Recompute committors for a few hundred configurations near q=0 using 10,000 instead of 100 velocity assignments and compare the p*_B distribution. If it does not sharpen into a peak at 1/2 — for NaCl, the paper already shows a wide 0-to-1 spread — the learned RC is not truly separating transition states. A second check: shoot trajectories from configurations lying on the claimed separatrix line of the (r_ion, G5_58) PMF; measure how often they commit to A vs B. An isocommittor surface would give 50/50 outcomes.

Watch

Extended reading notes

Core claim

The paper's central claim: a deep neural network trained on committor values, then interrogated with LIME/SHAP, identifies which collective variables actually define the reaction coordinate. For alanine dipeptide, q reproduces the committor with a peak at p_B=1/2, and attribution shows dihedral θ (with φ) dominates, the θ contribution growing near the transition state in a way global linear models miss. For NaCl in water, SHAP singles out two atom-centered symmetry functions (G5_58: O–Na–O shell at 2.0 Å; G5_1217: Na–Cl–O angular term) that, with r_ion, yield a well-defined p_B=0.5 separatrix. The review also finds that different hyperparameters give nearly identical RCs and feature rankings

Load-bearing premise

The framework rests on the committor estimates themselves: 100 one-picosecond trajectories with random thermal velocities must yield converged, unbiased p*_B labels for every sampled configuration; if those labels are noisy or biased, the learned RC and the XAI rankings inherit the error — and the NaCl results show that near the transition state the labels are widely scattered rather than sharply centered at 1/2.

Editorial extensions

If this is right

  • For alanine dipeptide, the RC is dominated by the dihedral θ (with φ), not ψ; the θ contribution sharpens near the TS, predicting a tilted separatrix line on the (φ,θ) free-energy surface at p*_B = 0.5.
  • For NaCl in water, the SHAP-identified ACSFs G5_58 and G5_1217, together with r_ion, are sufficient to build a 2D PMF with a well-defined TS line; these descriptors correlate with the physical water-bridging variables ρ and N_B, connecting abstract features to mechanism.
  • Because different DNN architectures (depth, width, regularization) produce nearly identical RCs and feature rankings, the identified mechanism is not an artifact of a particular trained model.
  • The framework extends likelihood-maximization RC methods by replacing a linear parametric ansatz with a flexible nonlinear map, while the XAI step recovers interpretability lost in the nonlinearity.
  • The review's protocol — sample, train on committor, explain with XAI — is offered as transferable to other rare-event systems (nucleation, protein conformational change) where the RC is unknown.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural stress-test: verify that the p*_B=0.5 separatrix in the identified 2D surface is actually an isocommittor surface (i.e., shooting from points along it reproduces p_B≈0.5 within error). The NaCl case already hints the learned RC may be imperfect, since committor values near q=0 are spread between 0 and 1 rather than sharply peaked.
  • The framework still depends on the preselected candidate CVs: if the true RC involves a coordinate not in the input set, the network cannot discover it. Integrating automated feature generation (e.g., graph-based descriptors) could close that gap.
  • The XAI attribution could be used online to guide adaptive sampling: focus new committor evaluations where the attributed dominant CVs are most uncertain, reducing the 100-trajectory cost per configuration.
  • The finding that G5_1217 increases toward the TS and then decreases implies a late-barrier solvent rearrangement; a time-resolved analysis of hydration-shell overlap near the separatrix would test whether this is a dynamic bottleneck.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. This paper is a review of the authors' own explainable deep-learning framework for identifying reaction coordinates (RCs) from committor values. It describes the methodology—committor sampling, cross-entropy minimization as a loss function, a deep neural network mapping candidate collective variables to an RC, and LIME/SHAP for feature attribution—and surveys applications to alanine dipeptide isomerization, hyperparameter tuning, and NaCl ion-pair dissociation in water. The central claim is that combining deep learning of the committor with XAI enables identification of dominant collective variables and shows that the committor distribution on the surface of those variables is separated by well-defined boundaries. The review explicitly states that no new data were generated.

Significance. If the framework performs as advertised, it would offer a practical, interpretable route to RC identification in high-dimensional molecular systems. The alanine dipeptide application is convincing: the learned RC gives a sharp p*_B = 1/2 peak near q=0, and LIME/SHAP consistently identify the dihedral angle θ (alongside φ) as dominant, with the attribution shifting to θ near the transition state. The hyperparameter study is a useful robustness check, showing that different architectures yield similar RCs and consistent feature attributions. However, the NaCl case—one of the two core demonstrations—fails the very committor histogram test the paper itself endorses, and the claimed 'well-defined separatrix' is derived from grid-averaged data rather than from configuration-resolved committor validation. As it stands, the broad claim in the abstract is not supported by the full body of evidence.

major comments (3)
  1. [Abstract; Section III C, Figure 9] The abstract claims that the approach 'demonstrates that the committor distribution on the surface using important CVs is separated by well-defined boundaries.' This is contradicted by the paper's own NaCl result: Section III C states that 'the committor values near q=0 are widely distributed between 0 and 1 and do not exhibit a clear sharp peak at p*_B=1/2.' According to the committor histogram test described in Section II A, a valid RC must produce a sharp peak at p*_B=1/2. The speculation that this failure is due to the large number of input variables and would be overcome by hyperparameter tuning is untested. The NaCl demonstration therefore does not support the advertised capability.
  2. [Section III C, Figure 11] The 'well-defined separatrix line' in Figure 11 is obtained by dividing the (r_ion, G5) plane into a 200×200 grid, averaging p*_B within each cell, and plotting the p*_B=0.5 contour after cubic interpolation. Grid-averaging can produce a smooth p*_B=0.5 contour even when individual configurations have p*_B scattered near 0 and 1, which is exactly the behavior shown in Figure 9 near q=0. The manuscript does not report per-configuration committor histograms on either side of the separatrix. Without such validation, the claim that G5_58 and G5_1217, together with r_ion, form a valid RC is not established.
  3. [Section III C; Section II D] The learned RC q is a function only of the 1,296 ACSF inputs; r_ion was not included as an input feature, as stated in Section III C ('two types of ACSFs ... were employed as CVs for the neural network inputs'). Nevertheless, the paper concludes that 'G5_58 or G5_1217 will appropriately represent the RC together with the interionic distance r_ion.' This inference is not supported by the model: the DNN never saw r_ion, so any role of r_ion in the RC is an external assumption. The 2D PMF plots in Figure 11 superimpose committor data on (r_ion, G5) but do not demonstrate that the learned q is a function of this pair. The manuscript should either include r_ion as an input feature or rephrase the conclusion to avoid claiming that r_ion is part of the identified RC.
minor comments (6)
  1. [Section III B] The sentence 'N_node most frequently converged to 5 and 3 in vacuum and in water, respectively' conflicts with the preceding description that N_node was searched from 100 to 5000. It appears that N_layer is meant. Please correct.
  2. [Throughout] There are numerous typos and encoding artifacts: 'depeptide' (Section III A heading), 'resepectively', 'adecuacy', 'hypearparameter', 'committer' for 'committor' in several places, 'rubust', 'are are' duplication, and 'Moveover'. Figure 12 contains placeholder characters '□' in place of Å^-3 and negative signs. These should be fixed.
  3. [Reference 141] The journal reference lists 'J. Chem. Phys.164, 164, 094101' with a duplicated page number; should be 'J. Chem. Phys.164, 094101 (2026)'.
  4. [Section III C] The phrase 'exhibits a second highest contribution following the RC' is unclear because r_ion was not an input. Clarify whether 'RC' refers to the predicted q or to a separately considered variable.
  5. [Section III A] The state definitions such as '(−150°,0°)≤(φ,ψ)≤(30°,180°)' are ambiguous; specify the intervals for φ and ψ separately to avoid confusion.
  6. [Section II E, Eq. (18)] The display of the SHAP kernel is mangled: 'MC|z''||z''|(M−|z''|)' should be typeset as the binomial-coefficient form. Please revise for readability.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the review summarizes the authors' own peer-reviewed applications, but the core committor-based derivation and CV-importance analysis are not reduced to their inputs by construction.

full rationale

The paper is a review of the authors' own framework. Section II builds a standard chain: committor values p*_B are evaluated from short MD trajectories; a neural network maps candidate CVs to an RC q by minimizing cross-entropy; LIME/SHAP then attribute feature importance. This is not circular because the training labels (p*_B) are measured independently and the model is tested on held-out data (e.g., Section III A uses a 5:1:4 train/validation/test split, and Figure 3 shows p*_B(q) on the test set). For alanine dipeptide, the conclusion that theta (rather than psi) is the dominant RC variable is anchored to the independent committor study of Bolhuis et al., not merely to the fitted model; the paper explicitly states agreement with that earlier result. The hyperparameter section likewise reports generalization RMSE on test data. In the NaCl case, the paper openly admits a validation weakness: 'the committor values near q=0 are widely distributed between 0 and 1 and do not exhibit a clear sharp peak at p*_B=1/2.' The later 'well-defined separatrix' in Figure 11 is constructed from grid-averaged, smoothed p*_B = 0.5 contours, which is a display choice and a limitation for the RC claim, but it is not a definitional reduction of a prediction to its own input; the underlying ACSF selection by SHAP is a genuine data-driven ranking of input features. Self-citations (Refs. 129, 131, 132, 141) are used to report prior data, methods, and figures, which is normal for a review, and no load-bearing argument invokes an unverified uniqueness theorem or ansatz from those citations. Thus no circular step meets the evidentiary bar; the paper's weaknesses are validity/correctness concerns rather than circularity.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

This review introduces no new physical entities. Its free parameters are mostly in the descriptor definitions (σ, coordination function parameters, ACSF grid) and the chosen neural network architecture. The central axioms are standard in transition path theory, with the main risk being sampling convergence of committor estimates and the validity of XAI attributions as proxies for mechanistic importance.

free parameters (4)
  • σ (Gaussian width in inter-ionic water density ρ) = r_ion/2 (system-dependent, not a universal constant)
    Eq. (7): σ controls the effective volume for counting water molecules and is chosen as r_ion/2, a functional form that is not derived from first principles; it affects the definition of ρ, which is used as a comparison CV.
  • a and b in ion–water coordination function f_s-w = a=3 Å⁻¹, b=3.2 Å
    Eq. (8): these parameters set the switching function for defining coordinated water molecules and are chosen ad hoc (though likely from prior work); they change the definition of N_B.
  • ACSF parameters η, R_s, ζ, λ, R_c = R_c=10.0 Å, η=2.0 Å⁻² (G2), η=1.2 Å⁻² (G5); many combinations of R_s, λ, ζ (total 1,296 CVs)
    Section III C: The ACSF descriptors are constructed by sampling a grid of these parameters. The resulting set is not independent of the choice of grid; the SHAP analysis then selects among them. The parameters themselves are not fitted to data, but the grid is a subjective choice.
  • Neural network architecture (layers, nodes, dropout, L2 regularization) = five hidden layers: 400, 200, 400, 200, 400 nodes; dropout 0.5
    Section II C: The architecture is fixed for the alanine and NaCl applications. Hyperparameter tuning (Section III B) shows multiple equivalent models, implying the architecture is not uniquely determined by the data.
assumptions (4)
  • domain assumption The committor is the ideal reaction coordinate, and a good RC yields p_B(q) that is a monotonically increasing sigmoidal function of q.
    Section II A: This is the conceptual foundation of the framework, rooted in transition path theory, but it is an idealization; real systems may have multiple channels or memory effects that violate the single-coordinate sigmoidal model.
  • domain assumption The cross-entropy loss with a sigmoidal model is a valid objective for learning the RC.
    Section II B: The derivation from KL divergence assumes the Bernoulli model for committor outcomes; this is standard but still a modeling assumption.
  • domain assumption SHAP and LIME feature attributions correctly identify the input variables that govern the neural network's predictions.
    Section II E: These XAI methods have known limitations; for nonlinear models, local attributions may not reflect global importance, and the paper does not validate them against ground-truth mechanisms.
  • domain assumption The committor values sampled from 1 ps trajectories with 100 velocity assignments are sufficiently converged estimates of the true committor.
    Section III A and III C: This is the key sampling assumption; the paper does not provide statistical error bars on p*_B values.

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Cite this review

Pith. "Pith review of Deep learning of committor and explainable artificial intelligence analysis for identifying reaction coordinates." pith.science (2026). https://pith.science/paper/IJRSNTLQ

@misc{pith2026260325237,
  author       = {Pith},
  title        = {Pith review of: Deep learning of committor and explainable artificial intelligence analysis for identifying reaction coordinates},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IJRSNTLQ}},
  note         = {Machine review of arXiv:2603.25237}
}
read the original abstract

In complex molecular systems, the reaction coordinate (RC) that characterizes transition pathways is essential to understand underlying molecular mechanisms. This review surveys a framework for identifying the RC by applying deep learning to the committor, which provides the most reliable measure of the progress along a transition path. The inputs to the neural network are collective variables (CVs) expressed as functions of atomic coordinates of the system, and the corresponding RC is predicted as the output by training the network on the committor as the learning target. Because deep learning models typically operate in a black-box manner, it is difficult to determine which input variables govern the predictions. The incorporation of eXplainable Artificial Intelligence (XAI) techniques enables quantitative assessment of the contributions of individual input variables to the predictions. This approach allows the identification of CVs that play dominant roles and demonstrates that the committor distribution on the surface using important CVs is separated by well-defined boundaries. The framework provides an explainable deep learning strategy for assigning a molecular mechanism from the RC and is applicable to a wide range of complex molecular systems.

Figures

Figures reproduced from arXiv: 2603.25237 by the authors.

Figure 1
Figure 1. FIG. 1. Schematic illustration of the explainable deep learning framework for identifying RCs based on committor [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2. (a) Atomic indices assigned to alanine dipeptide. The three major dihedral angles, [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3. (a) Relationship between the committer value [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: FIG. 4. Feature contribution of each CV in absolute value obtained using LIME (red) and SHAP (blue). From left to right: 0 [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: FIG. 5. Scatter plot for RMSEs between the predicted and reference [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: FIG. 6. Feature contributions of each CV in absolute value using [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: FIG. 7. (a) Structures within [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 9
Figure 9. Figure 9: FIG. 9. Committor [PITH_FULL_IMAGE:figures/full_fig_p010_9.png]
Figure 10
Figure 10. Figure 10: FIG. 10. Index dependence of feature contribution of each CV evaluated by the absolute SHAP value. Reproduced from Ke. Okada, Ka. Okada, [PITH_FULL_IMAGE:figures/full_fig_p011_10.png]
Figure 12
Figure 12. Figure 12: FIG. 12. Distribution of committor [PITH_FULL_IMAGE:figures/full_fig_p012_12.png]

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Pith tools

Reviewed August 2, 2026 · model on record in the stance chip above.