Title: FlashMD: long-stride, universal prediction of molecular dynamics
Venue: NeurIPS 2025
Accepted: yes
Keywords: molecular dynamics

atomistic modeling

geometric machine learning

statistical mechanics
Abstract: Molecular dynamics (MD) provides insights into atomic-scale processes by integrating over time the equations that describe the motion of atoms under the action of interatomic forces. Machine learning models have substantially accelerated MD by providing inexpensive predictions of the forces, but they remain constrained to minuscule time integration steps, which are required by the fast time scale of atomic motion. In this work, we propose FlashMD, a method to predict the evolution of positions and momenta over strides that are between one and two orders of magnitude longer than typical MD time steps. We incorporate considerations on the mathematical and physical properties of Hamiltonian dynamics in the architecture, generalize the approach to allow the simulation of any thermodynamic ensemble, and carefully assess the possible failure modes of a direct MD approach. We validate FlashMD’s accuracy in reproducing equilibrium and time‐dependent properties, using both system‐specific and general-purpose models, extending the ability of MD simulation to reach the long time scales needed to model microscopic processes of high scientific and technological relevance.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces FlashMD, a graph neural network-based framework for long-stride molecular dynamics (MD) prediction. Instead of integrating atomic trajectories with femtosecond-scale time steps, FlashMD directly predicts the positions and momenta of atoms at large time strides (up to 64 fs), bypassing both force evaluation and numerical integration. \n\nThe model is trained either on system-specific or universal MD trajectories generated using a machine-learned interatomic potential (PET-MAD), and it is designed to handle diverse thermodynamic ensembles (NVE, NVT, NpT). To stabilize predictions and maintain physical plausibility, the authors incorporate energy conservation at inference time, rotational and time-reversal augmentations during training, and uncertainty quantification to manage chaos and out-of-distribution behavior. \n\nFlashMD is evaluated across a wide set of systems—including water, biomolecules, metals, and solid-state electrolytes—and shows significant speed-ups over conventional MD with competitive accuracy.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- FlashMD bypasses the standard time-integration paradigm by directly learning large time step updates in atomic trajectories—offering acceleration up to 34× in realistic settings.\n- Universal generalization: The model is trained on a chemically diverse dataset (MAD) and evaluated across multiple systems and ensembles without fine-tuning, supporting the claim of universality.\n- The model includes mass-scaled targets, center-of-mass conservation, optional rotational data augmentation, and energy conservation enforcement during inference.\n- Strong empirical scope: Results span small molecules, biomolecules, and materials systems across different thermodynamic settings, with ablation studies and timing benchmarks.\n\nWeaknesses\n- The model is not symplectic by design. Although energy is conserved via momentum rescaling, phase space volume conservation and time reversibility are not guaranteed, raising questions about long-term stability and correctness.\n- Key prior models such as TrajCast or Equivariant Graph Neural Operators are not evaluated head-to-head on realistic benchmarks. Comparisons to MDNet and EGNN are restricted to toy systems (liquid argon).\n- Limited evidence for stability over long time horizons: The paper does not systematically assess long-time trajectory divergence, especially for chaotic systems. There is no quantitative analysis of error accumulation across multiple FlashMD steps.\n\nQUESTIONS:\n1.\tHow does FlashMD compare to TrajCast or other equivariant autoregressive GNNs on realistic systems (e.g., alanine dipeptide)?\n2.\tHow many steps can be chained in FlashMD before the trajectory diverges significantly from ground truth MD? Is there any cumulative error analysis?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 2, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces a deep learning model that predicts up to ~100 times larger time steps than MD simulation with machine learning force fields. The model is based on a GNN, which uses positions and velocities of the current state to predict a new configuration ~10-100 MD steps ahead. This potentially allows being faster than MD. The model is trained on a wide variety of systems, and can, hence, be evaluated even on unseen systems, which is demonstrated in the experiments. The investigated evaluation systems are water, alanine dipeptide in water, a metal surface, and a solid-state electrolyte, where the model recovers a phase transition.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The paper is well written and clearly motivated. Especially, MD simulations and potential pitfalls are in depth discussed.\n- Although the idea of predicting larger time steps with ML models is not novel, the proposed architecture with the filtering is. Moreover, the model seems to be much more transferable than previous models. \n- They evaluate the same trained model on a variety of systems, highlighting the transferability of the universal model\n- The model can be applied to different ensembles, e.g.. NVE, NVT, NPT.\n\nWeaknesses:\n- For the alanine dipeptide experiments, the positive phi states are not shown.\n- Results are mostly qualitative\n- Wall-clock speed of the method is unclear. Although FlashMD works with larger time steps, it might still be slower than using an MLIP, e.g., PET-MAD. It most likely is slower than classical MD, but might be more accurate.\n- See also questions\n\nQUESTIONS:\n- There is a lot of work on ML models that predict larger time steps than MD, which are not discussed in this work. Although the here presented approach is slightly different, they should be cited and discussed regardless. Examples include \"Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics\" by Klein et al. and \"Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics\" by Schreiner et al. \n- The PET-MAD MLIP is used throughout the paper, I think it would help to have a short section discussing this model\n-  The FlashMD framework section is a little short, a lot of important things, like the training, are only mentioned in the appendix. I am not sure that this can be fixed given the page limit, though.  \n- For the metal surface experiment: From figure 3c it seems that the transition temperature is predicted differently, while the text states that it matches. Why the discrepancy? Maybe there are some error bars that are not shown?\n- I expect that the systems the model was evaluated on were not seen during training for the universal model. Is this correct? And how similar are the systems in the training set?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis manuscript investigates the problem of learning a “universal” model for directly predicting state changes (coordinates and momenta) as an alternative approach to molecular dynamics (MD) simulations. Unlike previous methods such as MLIPs that focus on energy/force prediction or direct MD propagators on specific systems, the proposed approach aims for broader applicability and faster acceleration. The authors discuss several critical factors for training such a propagator and propose solutions, including the choice of GNN architecture, data augmentation strategies, and energy conservation during both training and inference. In experiments, they demonstrate that the proposed FlashMD model can accurately recover MD ensemble statistics for both single-system cases (e.g., water) and across diverse chemical systems.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n\n1. The manuscript is clearly written and well structured. The main objectives are effectively conveyed, and the authors provide a comprehensive review of the problem, in-depth discussions, and detailed results in the appendix. Overall, the clarity and  quality are high.\n2. The paper offers a solid review of the physical and mathematical foundations of molecular dynamics, demonstrating a strong grounding for the proposed method.\n3. The ablation studies are thorough, covering key factors such as training time steps and enforcement of energy conservation. Showing the importance of considering these key factors.\n4. Results show that a universal, direct MD propagator model can potentially capture the underlying physics across different systems.\n\n## Weaknesses\n\n1. A key limitation is the need to train separate models for different time strides, along with tedious hyperparameter tuning to find an appropriate stride that balances accuracy and speed. Especially for the “universal model,” optimal strides may vary across systems, this limits the model’s “universality” and practical applicability (significance). (Ref. Q1)\n\n1. Some modeling assumptions, though physically sound, may be overly restrictive. For instance, the insistence on determinism and strict Markovian behavior limits the model learn from NVE simulation data with complete observability. In practice, many MD datasets include missing data (e.g., coordinates only on non-solvent atoms) or follow alternative ensembles (e.g., NPT), which violates these assumptions. (Ref. Q2)\n\n1. The model is mainly evaluated against reference MD, with no comparison to other “direct MD propagator models”. This makes it difficult to assess the actual improvement of the proposed approach (Q4). Additionally, evaluations are conducted on small systems with short simulation times (Q5), limiting the scope of validation. (significance)\n\nQUESTIONS:\n1. Have the authors attempted to train a single model across multiple temporal scales? What are the potential challenges in learning such multi-timescale dynamics within one model?\n\n1. Can FlashMD be trained on MD datasets that do not follow the NVE ensemble or lack full observations (e.g., missing momenta, solvent state not saved)? How critical are the assumptions of determinism and strict Markovian dynamics in such cases?\n\n1. The authors evaluate energy conservation error for model selection but do not include it as part of the training loss. What are the main challenges in incorporating energy loss directly during training?\n\n1. All experiments are compared only against reference MD from PET-MAD. Why were no machine learning baselines, particularly other “direct MD propagators” discussed in Section 2.3, included for comparison?\n\n1. Scalability and generalization: Current evaluations seem to be limited to small systems (<1000 particles) and short simulations (ps-level). Have the authors tested the model on larger systems or longer timescales?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces FlashMD, a machine learning framework designed to accelerate Molecular Dynamics (MD) simulations. Instead of calculating forces at every minuscule time step, FlashMD uses a Graph Neural Network (GNN) to directly predict the positions and momenta of atoms over time strides that are up to two orders of magnitude longer than those used in traditional methods.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\\\n(1) Traditional MD approaches are typically known for their requirement of fine-grained time steps, leading to slow simulations. Approaches toward increasing strides should be encouraged. \\\n(2) The connection between MD and GNN is natural and worth investigating. \\\n**Weaknesses:**\\\n(1) Imprecise statements. \\\n1a) Line 55 is vague and can be misleading. The Hamilton's equations require energy conservation and are improper to directly define MD. Although the authors emphasize \"In its simplest form\" to make it more precise, this can still be misleading for researchers outside the research domain. \n1b) VV approximately conserves energy only because it is applied to an energy-conserved system -- VV can be used without energy conservation, and it is misleading to only emphasize that VV is suitable for NVE due to the ability to conserve energy with sufficiently small time steps. \\\n1c) MD is not necessarily deterministic. e.g., some simulations take Brownian motion into consideration, and some include quantum effects. \\\n(2) Unclear central point. The paper should more focus on the key contribution, which I presume is the orders-of-magnitude increase in time step sizes. However, in the framework section, I only see scattered bullet points and fail to observe their connections. I would recommend focusing on a key point within the 9 pages or submit elsewhere with higher page limits. At current stage, the main text of this paper looks like a list of contents in appendix.\n\nQUESTIONS:\n(1) What is the total simulation time of the result in Figure 3(a) shows? \\\n(2) The experiments typically use hundreds of atoms - however, for practical purposes, it is common to use millions of atoms. What is the scalability of this framework?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}