# Coarse-Graining Auto-Encoders for Molecular Dynamics

arXiv: https://arxiv.org/abs/1812.02706 · HTML: https://mlsanity.com/paper/1812.02706/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2018.

## Built on by (most cited first, 30 of 30)

- [Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations](https://mlsanity.com/paper/2210.07237/) (2022)
- [Machine Learning Coarse-Grained Potentials of Protein Thermodynamics](https://mlsanity.com/paper/2212.07492/) (2022)
- [Flow-matching -- efficient coarse-graining of molecular dynamics without forces](https://mlsanity.com/paper/2203.11167/) (2022)
- [Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-Scale Graph Networks](https://mlsanity.com/paper/2204.10348/) (2022)
- [Navigating protein landscapes with a machine-learned transferable coarse-grained model](https://mlsanity.com/paper/2310.18278/) (2023)
- [Generative Coarse-Graining of Molecular Conformations](https://mlsanity.com/paper/2201.12176/) (2022)
- [GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction](https://mlsanity.com/paper/2310.03030/) (2023)
- [chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics](https://mlsanity.com/paper/2408.15852/) (2024)
- [Attention-wise masked graph contrastive learning for predicting molecular property](https://mlsanity.com/paper/2206.08262/) (2022)
- [DiAMoNDBack: Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein Traces](https://mlsanity.com/paper/2307.12451/) (2023)
- [Predicting solvation free energies with an implicit solvent machine learning potential](https://mlsanity.com/paper/2406.00183/) (2024)
- [Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics](https://mlsanity.com/paper/2302.07071/) (2023)
- [Lipschitz-regularized gradient flows and generative particle algorithms for high-dimensional scarce data](https://mlsanity.com/paper/2210.17230/) (2022)
- [Multi-Scale Simulation of Complex Systems: A Perspective of Integrating Knowledge and Data](https://mlsanity.com/paper/2306.10275/) (2023)
- [Learning Physics-Consistent Particle Interactions](https://mlsanity.com/paper/2202.00299/) (2022)
- [Top-down machine learning of coarse-grained protein force-fields](https://mlsanity.com/paper/2306.11375/) (2023)
- [On the design space between molecular mechanics and machine learning force fields](https://mlsanity.com/paper/2409.01931/) (2024)
- [Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO(3)-Equivariant Autoencoders](https://mlsanity.com/paper/2310.02508/) (2023)
- [Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems](https://mlsanity.com/paper/2508.12569/) (2025)
- [Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials](https://mlsanity.com/paper/2510.11148/) (2025)
- [Automatic Identification of Chemical Moieties](https://mlsanity.com/paper/2203.16205/) (2022)
- [Constraint Decoupled Latent Diffusion for Protein Backmapping](https://mlsanity.com/paper/2410.13264/) (2024)
- [Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework without Data](https://mlsanity.com/paper/2504.20940/) (2025)
- [Investigation of Machine Learning-based Coarse-Grained Mapping Schemes for Organic Molecules](https://mlsanity.com/paper/2209.12946/) (2022)
- [Developing Machine-Learned Potentials for Coarse-Grained Molecular Simulations: Challenges and Pitfalls](https://mlsanity.com/paper/2209.12948/) (2022)
- [An Iterative Framework for Generative Backmapping of Coarse Grained Proteins](https://mlsanity.com/paper/2505.18082/) (2025)
- [Neural Upscaling from Residue-level Protein Structure Networks to Atomistic Structure](https://mlsanity.com/paper/2109.06700/) (2021)
- [Deep Signature: Characterization of Large-Scale Molecular Dynamics](https://mlsanity.com/paper/2410.02847/) (2024)
- [Universally applicable and tunable graph-based coarse-graining for Machine learning force fields](https://mlsanity.com/paper/2504.01973/) (2025)
- [GenPANIS: A Latent-Variable Generative Framework for Forward and Inverse PDE Problems in Multiphase Media](https://mlsanity.com/paper/2602.14642/) (2026)

## Abstract (arXiv)

Molecular dynamics simulations provide theoretical insight into the microscopic behavior of materials in condensed phase and, as a predictive tool, enable computational design of new compounds. However, because of the large temporal and spatial scales involved in thermodynamic and kinetic phenomena in materials, atomistic simulations are often computationally unfeasible. Coarse-graining methods allow simulating larger systems, by reducing the dimensionality of the simulation, and propagating longer timesteps, by averaging out fast motions. Coarse-graining involves two coupled learning problems; defining the mapping from an all-atom to a reduced representation, and the parametrization of a Hamiltonian over coarse-grained coordinates. Multiple statistical mechanics approaches have addressed the latter, but the former is generally a hand-tuned process based on chemical intuition. Here we present Autograin, an optimization framework based on auto-encoders to learn both tasks simultaneously. Autograin is trained to learn the optimal mapping between all-atom and reduced representation, using the reconstruction loss to facilitate the learning of coarse-grained variables. In addition, a force-matching method is applied to variationally determine the coarse-grained potential energy function. This procedure is tested on a number of model systems including single-molecule and bulk-phase periodic simulations.