mlsanityData to 7 Oct 2026

Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics

arXiv 2302.07071 ↗First posted 14 Feb 2023physics.chem-ph physics.bio-ph physics.comp-phcited by 9

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Abstract (arXiv)

Machine-learned coarse-grained (CG) models have the potential for simulating large molecular complexes beyond what is possible with atomistic molecular dynamics. However, training accurate CG models remains a challenge. A widely used methodology for learning CG force-fields maps forces from all-atom molecular dynamics to the CG representation and matches them with a CG force-field on average. We show that there is flexibility in how to map all-atom forces to the CG representation, and that the most commonly used mapping methods are statistically inefficient and potentially even incorrect in the presence of constraints in the all-atom simulation. We define an optimization statement for force mappings and demonstrate that substantially improved CG force-fields can be learned from the same simulation data when using optimized force maps. The method is demonstrated on the miniproteins Chignolin and Tryptophan Cage and published as open-source code.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 19 of its 100 references that are in the map. First posted on arXiv 14 Feb 2023.

Builds on

19 of 100 references.

References · 19

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,140
  3. 2014SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectivescited by 401
  4. 2017SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradientcited by 218
  5. 2022TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentialscited by 179
  6. 2013Minimizing Finite Sums with the Stochastic Average Gradientcited by 164
  7. 2017OSQP: An Operator Splitting Solver for Quadratic Programscited by 156
  8. 2022Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulationscited by 92
  9. 2018Machine Learning of coarse-grained Molecular Dynamics Force Fieldscited by 68
  10. 2020Coarse Graining Molecular Dynamics with Graph Neural Networkscited by 56
  11. 2018On the Ineffectiveness of Variance Reduced Optimization for Deep Learningcited by 48
  12. 2022Machine Learning Coarse-Grained Potentials of Protein Thermodynamicscited by 37
7 more
  1. 2022Flow-matching -- efficient coarse-graining of molecular dynamics without forcescited by 36
  2. 2018Coarse-Graining Auto-Encoders for Molecular Dynamicscited by 30
  3. 2021Learning neural network potentials from experimental data via Differentiable Trajectory Reweightingcited by 26
  4. 2021Machine Learning Implicit Solvation for Molecular Dynamicscited by 16
  5. 2022Contrastive Learning of Coarse-Grained Force Fieldscited by 7
  6. 2016Predictive Coarse-Grainingcited by 4
  7. 2022Developing Machine-Learned Potentials for Coarse-Grained Molecular Simulations: Challenges and Pitfallscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 2; 2025: 3; 2026: 1.

9 papers.

  1. 2025Consistent Sampling and Simulation: Molecular Dynamics with Energy-Based Diffusion Modelscited by 23
  2. 2023Navigating protein landscapes with a machine-learned transferable coarse-grained modelcited by 22
  3. 2023Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamicscited by 14
  4. 2023DiAMoNDBack: Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein Tracescited by 10
  5. 2024AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamicscited by 3
  6. 2024Thermodynamic Transferability in Coarse-Grained Force Fields using Graph Neural Networkscited by 2
  7. 2025Universally applicable and tunable graph-based coarse-graining for Machine learning force fieldscited by 0
  8. 2025Operator Forces For Coarse-Grained Molecular Dynamicscited by 0
  9. 2026Temperature transferable Machine Learned Coarse Grained model for proteinscited by 0