Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics | Paper Citation Record

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This paper cites Computer simulation of liquids.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Computer simulation of liquids

Reference 1

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This paper cites Understanding molecular simulation: from algorithms to applications.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Understanding molecular simulation: from algorithms to applications

Reference 2

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This paper cites The martini force field: coarse grained model for biomolecular simulations.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics The martini force field: coarse grained model for biomolecular simulations

Reference 3

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Observation 697ff453-97fa-4e87-92f2-c0cf2b297fce · outbound

This paper cites Coarse-grained protein models and their applications.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Coarse-grained protein models and their applications

Reference 4

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Observation 62c3973c-0def-4e71-a221-1135d14168c1 · outbound

This paper cites Uncertainty driven active learning of coarse grained free energy models.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Uncertainty driven active learning of coarse grained free energy models

Reference 5

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Observation f9cad4ec-09c4-4c21-9c28-da601f0b60e5 · outbound

This paper cites K-means clustering coarse-graining (kmc-cg): A next generation methodology for determining optimal coarse-grained mappings of large biomolecules.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics K-means clustering coarse-graining (kmc-cg): A next generation methodology for determining optimal coarse-grained mappings of large biomolecules

Reference 6

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Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Unresolved cited work

Reference 7

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Observation 083475fa-5f79-4c4f-b18c-5e42e2f15315 · outbound

This paper cites Insight into the density-dependence of pair potentials for predictive coarse-grained models.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Insight into the density-dependence of pair potentials for predictive coarse-grained models

Reference 8

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Observation dd32ad99-7c9f-45ad-be08-44183465f838 · outbound

This paper cites Unveiling interactions of a peptide-bound monolayer-protected metal nanocluster with a lipid bilayer.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Unveiling interactions of a peptide-bound monolayer-protected metal nanocluster with a lipid bilayer

Reference 9

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Observation 82da2f1c-73fb-4a0a-9c64-64a451fbdf45 · outbound

This paper cites Evolutionary algorithm in the optimization of a coarse-grained force field.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Evolutionary algorithm in the optimization of a coarse-grained force field

Reference 10

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Observation 55031dcf-4ace-475e-8804-b2885d14755f · outbound

This paper cites Temporally coher- ent backmapping of molecular trajectories from coarse-grained to atomistic resolution.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Temporally coher- ent backmapping of molecular trajectories from coarse-grained to atomistic resolution

Reference 11

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Observation d853fc4f-6eb0-4638-81f8-f5fd59f9d971 · outbound

This paper cites Multiscale coarse graining of liquid-state systems.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Multiscale coarse graining of liquid-state systems

Reference 12

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Observation c296a3af-b552-48ad-95ec-edac06fb714c · outbound

This paper cites Structure of a tractable stochastic mimic of soft particles.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Structure of a tractable stochastic mimic of soft particles

Reference 13

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Observation 48d539a2-5c88-432e-bc6a-9509bac03f5b · outbound

This paper cites Derivation of coarse-grained potentials via multistate iterative boltzmann inversion.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Derivation of coarse-grained potentials via multistate iterative boltzmann inversion

Reference 14

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Observation 4ada63d6-bd53-4692-b198-356e3b71cdfd · outbound

This paper cites Bottom-up coarse-grained models that accurately describe the structure, pressure, and compressibility of molecular liquids.The Journal of chemical physics, 143(24), 2015.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Bottom-up coarse-grained models that accurately describe the structure, pressure, and compressibility of molecular liquids.The Journal of chemical physics, 143(24), 2015

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:03.433816Z

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Observation 83b11a80-3b50-4dc0-b9b7-a1bcc47d9327 · outbound

This paper cites Transfer-learning-based coarse-graining method for simple fluids: toward deep inverse liquid-state theory.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Transfer-learning-based coarse-graining method for simple fluids: toward deep inverse liquid-state theory

Reference 16

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Observation 5b8a80c5-350c-48db-9062-8275dac1ee6e · outbound

This paper cites Influence of topology on effective potentials: coarse-graining ring polymers.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Influence of topology on effective potentials: coarse-graining ring polymers

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:03.391554Z

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Observation a232f74c-a752-4a78-89ac-dc4ba2313abd · outbound

This paper cites Effective surface coverage of coarse- grained soft matter.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Effective surface coverage of coarse- grained soft matter

Reference 18

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Observation 208db526-f0b3-493d-babc-e3832ee263f7 · outbound

This paper cites Solvent entropy and coarse-graining of polymer lattice models.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Solvent entropy and coarse-graining of polymer lattice models

Reference 19

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Observation 725ddac8-5aad-44e0-b50a-adeacff098f4 · outbound

This paper cites Integrating machine learning in the coarse-grained molecular simulation of polymers.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Integrating machine learning in the coarse-grained molecular simulation of polymers

Reference 20

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Observation 7cbc45b2-1264-4a32-9a43-238cfae1a098 · outbound

This paper cites Coarse grained protein- lipid model with application to lipoprotein particles.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Coarse grained protein- lipid model with application to lipoprotein particles

Reference 21

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Observation be08fb6a-c3c6-4b7b-9ecf-c9cb2bad9660 · outbound

This paper cites Coarse-graining methods for computational biology.Annual review of biophysics, 42(1):73–93, 2013.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Coarse-graining methods for computational biology.Annual review of biophysics, 42(1):73–93, 2013

Reference 22

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Observation 349df33d-3b32-47ee-9b7e-7e56469ba05f · outbound

This paper cites Perspective: Coarse-grained models for biomolecular systems.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Perspective: Coarse-grained models for biomolecular systems

Reference 23

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Observation 09abad8a-ec72-4d7c-96e9-b9bf0fe43c90 · outbound

This paper cites Flow-matching: Effi- cient coarse-graining of molecular dynamics without forces.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Flow-matching: Effi- cient coarse-graining of molecular dynamics without forces

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:03.224167Z

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Observation b859dc57-5a79-46a0-9db1-4ebd35462870 · outbound

This paper cites Exploring the landscape of model representations.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Exploring the landscape of model representations

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:03.200864Z

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Observation 0f0e9b50-af20-4a2d-947e-3062d929dcfe · outbound

This paper cites A data-driven perspective on the hierarchical assembly of molecular structures.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics A data-driven perspective on the hierarchical assembly of molecular structures

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:03.171567Z

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Observation 078cbdc1-f779-4f74-b167-9cfb110f28fe · outbound

This paper cites Openmscg: A software tool for bottom-up coarse-graining.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Openmscg: A software tool for bottom-up coarse-graining

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:03.148517Z

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Observation dcac804b-e081-47e6-a939-24c5d1fe2dd6 · outbound

This paper cites Graph neural network based coarse-grained mapping prediction.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Graph neural network based coarse-grained mapping prediction

Reference 28

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Observation 83fcdf73-56a6-4108-912c-5265ad648ae8 · outbound

This paper cites Coarse-graining auto-encoders for molecular dynamics.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Coarse-graining auto-encoders for molecular dynamics

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:03.100150Z

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Observation 0f365bb1-1a18-4137-b5c3-52d0b1fa0808 · outbound

This paper cites Martini 3: a general purpose force field for coarse-grained molecular dynamics.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Martini 3: a general purpose force field for coarse-grained molecular dynamics

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:03.075364Z

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Observation 75282f5f-c1cc-4f0d-a3c1-05bbd3280572 · outbound

This paper cites Machine learning coarse- grained potentials of protein thermodynamics.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Machine learning coarse- grained potentials of protein thermodynamics

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:03.058221Z

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Observation 9aec5fab-29cf-44f6-bf37-9a778c1a6094 · outbound

This paper cites Cgcompiler: auto- mated coarse-grained molecule parametrization via noise-resistant mixed-variable optimization.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Cgcompiler: auto- mated coarse-grained molecule parametrization via noise-resistant mixed-variable optimization

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:03.040576Z

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Observation 02e61e0d-d939-4095-be00-1ed928779fac · outbound

This paper cites Automated coarse-grained mapping algorithm for the martini force field and benchmarks for membrane–water partitioning.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Automated coarse-grained mapping algorithm for the martini force field and benchmarks for membrane–water partitioning

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:03.024025Z

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Observation 3ba451bb-5a2f-4206-8ce8-368fafed631d · outbound

This paper cites Generative Coarse-Graining of Molecular Conformations.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Generative Coarse-Graining of Molecular Conformations

Reference 34

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Observation c9c66aed-6c2f-41b8-90aa-ceb97cd18bb2 · outbound

This paper cites Coarsenconf: Equivariant coarsening with aggre- gated attention for molecular conformer generation.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Coarsenconf: Equivariant coarsening with aggre- gated attention for molecular conformer generation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:03.004442Z

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Observation ee13521e-e455-47cf-b640-c37911ea8c6c · outbound

This paper cites An information-theory- based approach for optimal model reduction of biomolecules.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics An information-theory- based approach for optimal model reduction of biomolecules

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.986024Z

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Observation 387b290e-6b62-4e8d-af40-1eb2471c7633 · outbound

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Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Unresolved cited work

Reference 37

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Observation c56a2dc7-a403-4e74-ac80-1f5380a38161 · outbound

This paper cites Graph theory meets ab initio molecular dynamics: Atomic structures¡? format?¿ and transformations at the nanoscale.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Graph theory meets ab initio molecular dynamics: Atomic structures¡? format?¿ and transformations at the nanoscale

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.950547Z

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Observation 9fb61996-c335-4285-8e52-2e1f616ffb45 · outbound

This paper cites Deepcg: Constructing coarse-grained models via deep neural networks.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Deepcg: Constructing coarse-grained models via deep neural networks

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.934025Z

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Observation 56c13136-5e7b-4bd1-9bb2-23eb251d8d5d · outbound

This paper cites Machine-learned coarse- grained models.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Machine-learned coarse- grained models

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.912460Z

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Observation 42ae0bfe-eb44-4df1-95de-b1f0c28a2b8a · outbound

This paper cites Neural network based prediction of conformational free energies-a new route toward coarse-grained simulation models.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Neural network based prediction of conformational free energies-a new route toward coarse-grained simulation models

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.894606Z

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Observation 7eeff86f-e5fd-4652-a529-6898d9f30b86 · outbound

This paper cites Machine learning of coarse-grained molecular dynamics force fields.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Machine learning of coarse-grained molecular dynamics force fields

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.873136Z

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Observation 7b302118-1f23-4d46-984d-d068d4d15cb6 · outbound

This paper cites Investigating molecu- lar kinetics by variationally optimized diffusion maps.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Investigating molecu- lar kinetics by variationally optimized diffusion maps

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.853747Z

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Observation 690a85bd-e893-4974-b3a8-8bdd4f7e417e · outbound

This paper cites Deep coarse-grained potentials via relative entropy minimization.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Deep coarse-grained potentials via relative entropy minimization

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.830305Z

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Observation 89205b48-cba0-4bd9-8ed0-942238247fc5 · outbound

This paper cites Graph-based approach to systematic molecular coarse-graining.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Graph-based approach to systematic molecular coarse-graining

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.795883Z

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Observation 3d30ea0c-c9cb-4dd8-8a38-d72e3cf619c4 · outbound

This paper cites Encoding and selecting coarse-grain mapping operators with hierarchical graphs.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Encoding and selecting coarse-grain mapping operators with hierarchical graphs

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.769725Z

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Observation 7ee3d264-9453-4a98-92a0-97fb0c445b11 · outbound

This paper cites Bottom-up coarse-graining: Principles and perspectives.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Bottom-up coarse-graining: Principles and perspectives

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.746906Z

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Observation 3542a172-e243-4ae1-a82e-1346c268ae10 · outbound

This paper cites Top-down machine learning of coarse- grained protein force fields.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Top-down machine learning of coarse- grained protein force fields

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.724765Z

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Observation fde1e475-60af-4e04-8e93-3b488fb01af3 · outbound

This paper cites Machine learning implicit solvation for molecular dynamics.The Journal of Chemical Physics, 155(8), 2021.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Machine learning implicit solvation for molecular dynamics.The Journal of Chemical Physics, 155(8), 2021

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.698209Z

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Observation 5874f445-caac-4a41-a2e8-8e95950d6f72 · outbound

This paper cites Physnet: A neural network for predicting energies, forces, dipole moments, and partial charges.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Physnet: A neural network for predicting energies, forces, dipole moments, and partial charges

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.673312Z

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Observation 362d6e34-a44b-49c8-b876-dc4c1c36d5b2 · outbound

This paper cites Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:16:02.216974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 543948e8-0b6a-42c4-a128-9193a8bc0f2e · outbound

This paper cites Machine learning for molecular simulation.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Machine learning for molecular simulation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.653721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3b2a3ecb-1ed9-47bf-8438-14f2dd4fd0e6 · outbound

This paper cites Multi- body effects in a coarse-grained protein force field.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Multi- body effects in a coarse-grained protein force field

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.633292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9b4d93ec-714a-4056-a35e-17e09ffc9c6a · outbound

This paper cites The multiscale coarse-graining method.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics The multiscale coarse-graining method

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.607331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 447896a5-0b22-4749-8029-f1210f610de4 · outbound

This paper cites Coarse graining molecular dynamics with graph neural networks.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Coarse graining molecular dynamics with graph neural networks

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.587687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ce396737-4cb9-4956-a6ee-46b3da365c13 · outbound

This paper cites Machine learned coarse- grained protein force-fields: Are we there yet? Current opinion in structural biology , 79:102533, 2023.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Machine learned coarse- grained protein force-fields: Are we there yet? Current opinion in structural biology , 79:102533, 2023

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.570757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0289db66-4899-4e05-b71b-afdfe3a96f48 · outbound

This paper cites Navigating protein landscapes with a machine-learned transferable coarse-grained model.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Navigating protein landscapes with a machine-learned transferable coarse-grained model

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T15:16:02.001124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4030aae-32d6-472b-a384-853e8cb5dabe · outbound

This paper cites Many-Body Coarse-Grained Molecular Dynamics with the Atomic Cluster Expansion.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Many-Body Coarse-Grained Molecular Dynamics with the Atomic Cluster Expansion

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:16:02.163756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f6bf134b-7891-48c2-9c70-a6950677566a · outbound

This paper cites Mace: Higher order equivariant message passing neural networks for fast and accurate force fields.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Mace: Higher order equivariant message passing neural networks for fast and accurate force fields

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.549894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 493e087a-4934-40c0-a2a5-49a1ec49bce1 · outbound

This paper cites Crash testing machine learning force fields for molecules, materials, and interfaces: Model analysis in the tea challenge 2023.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Crash testing machine learning force fields for molecules, materials, and interfaces: Model analysis in the tea challenge 2023

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.530751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 60da65ca-8acf-421d-b74c-eddc2e5d2c21 · outbound

This paper cites Projection of diffusions on submanifolds: Application to mean force computation.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Projection of diffusions on submanifolds: Application to mean force computation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.508415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0d95f316-a8a9-4636-b513-2ebebf18290a · outbound

This paper cites Evaluation of the mace force field architecture: From medicinal chemistry to materials science.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Evaluation of the mace force field architecture: From medicinal chemistry to materials science

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.489590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 08c83f7d-2ca5-40e4-931d-d7baf6a15c0d · outbound

This paper cites Trans- ferability of data sets between machine-learned interatomic potential algorithms.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Trans- ferability of data sets between machine-learned interatomic potential algorithms

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.470912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 31771723-762a-47c6-895e-4494d7b085fb · outbound

This paper cites Transferable machine learn- ing interatomic potential for bond dissociation energy prediction of drug-like molecules.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Transferable machine learn- ing interatomic potential for bond dissociation energy prediction of drug-like molecules

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.451705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 49e4af52-9216-4d75-a279-c1c28406b268 · outbound

This paper cites Featured graph coarsening with similarity guarantees.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Featured graph coarsening with similarity guarantees

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.435790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b2aaf5cd-fa8b-4c6f-be23-861ad248cb63 · outbound

This paper cites A unified framework for optimization-based graph coarsening.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics A unified framework for optimization-based graph coarsening

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.419696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ae7f9903-2747-48fd-bc25-3003afce7ee9 · outbound

This paper cites Optimization frame- work for semi-supervised attributed graph coarsening.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Optimization frame- work for semi-supervised attributed graph coarsening

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.401001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0751b5d9-302a-4233-bf1f-52ea2ab6c3bb · outbound

This paper cites Multi-component coarsened graph learning for scaling graph machine learning.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Multi-component coarsened graph learning for scaling graph machine learning

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.382454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b37bb22c-5b18-4b06-81ea-cb9b6f346d1d · outbound

This paper cites Graph coarsening with preserved spectral properties.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Graph coarsening with preserved spectral properties

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.358886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 92cbbe43-d248-4d0d-b041-59dc9833c93b · outbound

This paper cites Graph reduction with spectral and cut guarantees.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Graph reduction with spectral and cut guarantees

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.341076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0d9a3c2b-89bb-4d0a-8f92-fe59ca770bef · outbound

This paper cites an unresolved cited work.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-08-06T15:16:02.324389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f8f7b251-3540-484c-8151-debff387bbfc · outbound

This paper cites The atomic simu- lation environment—a python library for working with atoms.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics The atomic simu- lation environment—a python library for working with atoms

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.305655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7d7cbce5-a69d-4268-b8b1-8dd29b772136 · outbound

This paper cites On the role of gradients for machine learning of molecular energies and forces.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics On the role of gradients for machine learning of molecular energies and forces

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.284238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 24c45759-6cb4-4926-b509-5c45b98f36fe · outbound

This paper cites Thermodynamic transferability in coarse-grained force fields using graph neural networks.

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Thermodynamic transferability in coarse-grained force fields using graph neural networks

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:16:02.261879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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