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     Quick Explanation


    Quick visual summary: Plotting ViSNet performance on the revised MD17 (rMD17) molecules shows sub-0.05 kcal/mol energy MAE for most molecules and sub-0.18 kcal/(molΒ·Γ…) force MAE β€” state-of-the-art accuracy on rMD17 as reported by ViSNet (Nature Communications) with the rMD17 numbers provided in the paper's supplementary material and data links.

    Sources: ViSNet (Nature Communications) rMD17 results and rMD17 dataset availability.




     Long Explanation


    rMD17: Visualizing reported ViSNet errors (rMD17 subset)

    Below are interactive Plotly graphs built from reported rMD17 metrics extracted from the ViSNet paper (Nature Communications). Each plot is a unique div with immediate Plotly.newPlot() code so it renders in-stream. Data source: ViSNet supplementary/extracted table (rMD17 metrics). Visualize first β€” explain second.

    Concise interpretation (evidence-cited)

    ViSNet achieves very low energy MAE on rMD17 β€” many molecules show energy MAE <0.05 kcal/mol and force MAE often <0.18 kcal/(molΒ·Γ…), indicating ab initio–level accuracy for these small-molecule benchmarks as reported by the authors. These numbers come directly from ViSNet's reported rMD17 table and supplement

    Context & related work (uncertainty-aware methods)

    For uncertainty-aware interatomic force modeling, a Bayesian NequIP variant (equivariant Bayesian neural networks with adaptive SGHMC) shows that posterior-sampling approaches can produce calibrated uncertainties and strong accuracy on MD17-derived datasets, but training/calibration limitations exist (overconfidence in some regimes and long compute time) β€” relevant when using rMD17 for active learning or MD with uncertainty-aware surrogates.

    Caveats & blindspots
    • rMD17 is a revised/cleaned subset of MD17 β€” reported errors are dataset- and training-scheme–dependent; cross-paper comparisons must ensure identical train/test splits and sample counts ().
    • Low MAE on small molecules does not guarantee generalization to large biomolecules; ViSNet authors explicitly discuss scaling/robustness limits and the need for more tests on large systems ().
    • Uncertainty quantification (UQ) techniques (e.g., Bayesian NequIP) improve calibration but have compute and calibration trade-offs; see cited Bayesian NequIP work for guidance on MC samples vs calibration ().

    Data & code links: ViSNet code and chignolin data: GitHub; rMD17 dataset archive link in ViSNet supplement and in Bayesian NequIP ESI ( ).



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    Updated: March 17, 2026

     Top Data Sources ExportMCP


     Analysis Wizard



    Preparing plotting-ready rMD17 arrays and producing per-molecule Energy vs Force MAE scatter and bar plots from ViSNet supplementary data (rMD17), enabling immediate visualization and model-comparison.



     Hypothesis Graveyard


    Hypothesis: MD17/rMD17 performance fully predicts large-biomolecule accuracy β€” falsified because ViSNet authors caution generalization limits and dataset-based biases; small-molecule metrics don't guarantee performance on proteins (paper limitations).


    Hypothesis: Single-sample posterior approximations give reliable UQ β€” falsified by Bayesian NequIP showing one sample yields poor calibration and multiple MC samples are required.

     Science Art


    rMD17 Science Art

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