Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties | Paper Citation Record

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This paper cites No free lunch theorems for optimization.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties No free lunch theorems for optimization

Reference 1

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This paper cites T.; Frost, J.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties T.; Frost, J

Reference 2

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This paper cites Extending Solid-State Calculations to Ultra-Long-Range Length Scales.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Extending Solid-State Calculations to Ultra-Long-Range Length Scales

Reference 3

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This paper cites Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning

Reference 4

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Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Unresolved cited work

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This paper cites P.; Hong, S.; Islam, M.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties P.; Hong, S.; Islam, M

Reference 6

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This paper cites K.; Casewit, C.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties K.; Casewit, C

Reference 7

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This paper cites V.; Woellner, C.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties V.; Woellner, C

Reference 8

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This paper cites Characterizing mechanical properties of graphite using molecular dynamics simulation.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Characterizing mechanical properties of graphite using molecular dynamics simulation

Reference 9

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This paper cites Machine-learned potentials for next-generation matter simulations.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Machine-learned potentials for next-generation matter simulations

Reference 10

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This paper cites S.; Nebgen, B.; Messerly, R.; Li, Y.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties S.; Nebgen, B.; Messerly, R.; Li, Y

Reference 11

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This paper cites Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations

Reference 12

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This paper cites T.; Chmiela, S.; Sauceda, H.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties T.; Chmiela, S.; Sauceda, H

Reference 13

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This paper cites R.; Lahiri, I.; Suresh, K.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties R.; Lahiri, I.; Suresh, K

Reference 14

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This paper cites Development of a machine learning potential for graphene.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Development of a machine learning potential for graphene

Reference 15

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This paper cites Reliable machine learning potentials based on artificial neural network for graphene.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Reliable machine learning potentials based on artificial neural network for graphene

Reference 16

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This paper cites L.; Cai, C.; Lin, Y.; Wang, B.; Xu, J.; Zhu, J.-X.; Luo, C.; Zhang, Y.; Goodall, R.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties L.; Cai, C.; Lin, Y.; Wang, B.; Xu, J.; Zhu, J.-X.; Luo, C.; Zhang, Y.; Goodall, R

Reference 17

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This paper cites Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

Reference 18

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This paper cites P.; Aktulga, H.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties P.; Aktulga, H

Reference 19

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Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Unresolved cited work

Reference 20

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This paper cites J.; Chen, J.; Dułak, M.; Ferrighi, L.; Gavnholt, J.; Glinsvad, C.; Haikola, V.; Hansen, H.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties J.; Chen, J.; Dułak, M.; Ferrighi, L.; Gavnholt, J.; Glinsvad, C.; Haikola, V.; Hansen, H

Reference 21

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This paper cites afer, C.; J\'onsson, E. O.; Hermes, E. D.; Nilsson, F. A.; Kastlunger, G.; Levi, G.; J\'onsson, H.; H\.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties afer, C.; J\'onsson, E. O.; Hermes, E. D.; Nilsson, F. A.; Kastlunger, G.; Levi, G.; J\'onsson, H.; H\

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This paper cites E.; Christensen, R.; Dułak, M.; Friis, J.; Groves, M.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties E.; Christensen, R.; Dułak, M.; Friis, J.; Groves, M

Reference 23

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Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Unresolved cited work

Reference 24

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Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Unresolved cited work

Reference 25

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This paper cites Understanding molecular simulation, 2nd ed.; Computational science series 1; Acad.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Understanding molecular simulation, 2nd ed.; Computational science series 1; Acad

Reference 26

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Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Hoover NPT dynamics for systems varying in shape and size

Reference 27

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This paper cites Constrained systems and statistical distribution.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Constrained systems and statistical distribution

Reference 28

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This paper cites End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems

Reference 29

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This paper cites In Computer Vision - ECCV 2016; Matas, J., Sebe, N., Welling, M., Eds.; Lecture Notes in Computer Science Ser.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties In Computer Vision - ECCV 2016; Matas, J., Sebe, N., Welling, M., Eds.; Lecture Notes in Computer Science Ser

Reference 30

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This paper cites Deep Learning with Python, Second Edition; Manning Publications Co.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Deep Learning with Python, Second Edition; Manning Publications Co

Reference 31

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This paper cites Application of atomic stress to compute heat flux via molecular dynamics for systems with many-body interactions.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Application of atomic stress to compute heat flux via molecular dynamics for systems with many-body interactions

Reference 32

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Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Methodology and meaning of computing heat flux via atomic stress in systems with constraint dynamics

Reference 33

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Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Implementation strategies in phonopy and phono3py

Reference 34

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This paper cites M.; PASKIN, A.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties M.; PASKIN, A

Reference 35

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This paper cites Computing vibrational spectra from ab initio molecular dynamics.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Computing vibrational spectra from ab initio molecular dynamics

Reference 36

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Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Velocity-autocorrelation spectrum of simple classical liquids

Reference 37

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This paper cites H.; Yu, T.; Lu, Y.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties H.; Yu, T.; Lu, Y

Reference 38

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This paper cites Investigation of strain-induced modulation on electronic properties of graphene field effect transistor.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Investigation of strain-induced modulation on electronic properties of graphene field effect transistor

Reference 39

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Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Unresolved cited work

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Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties R.; Millman, K

Reference 41

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Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Unresolved cited work

Reference 42

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Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Unresolved cited work

Reference 43

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This paper cites A review of linear carbon chains.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties A review of linear carbon chains

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:29.796137Z

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This paper cites Mechanical properties of carbyne: experiment and simulations.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Mechanical properties of carbyne: experiment and simulations

Reference 45

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This paper cites Preparation of long monatomic carbon chains: Molecular dynamics studies.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Preparation of long monatomic carbon chains: Molecular dynamics studies

Reference 46

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This paper cites Perpendicular growth of carbon chains on graphene from first-principles.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Perpendicular growth of carbon chains on graphene from first-principles

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:29.749741Z

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This paper cites D.; Rajendran, S.; Liew, K.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties D.; Rajendran, S.; Liew, K

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:29.734995Z

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This paper cites Research on the auxetic behavior and mechanical properties of periodically rotating graphene nanostructures.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Research on the auxetic behavior and mechanical properties of periodically rotating graphene nanostructures

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:29.719269Z

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Observation de8e1fa9-fe91-469d-b25c-8509e0471df7 · outbound

This paper cites Numerical Investigation of the Fracture Mechanism of Defective Graphene Sheets.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Numerical Investigation of the Fracture Mechanism of Defective Graphene Sheets

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:29.701010Z

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Observation 94cdcc80-9a8e-4a57-b892-084f0f704f36 · outbound

This paper cites Ab initio calculation of ideal strength and phonon instability of graphene under tension.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Ab initio calculation of ideal strength and phonon instability of graphene under tension

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:29.682789Z

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Observation 54cfac99-0ff4-4c8a-848c-01eabdac791b · outbound

This paper cites Raman Spectroscopy of nanomaterials: How spectra relate to disorder, particle size and mechanical properties.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Raman Spectroscopy of nanomaterials: How spectra relate to disorder, particle size and mechanical properties

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:29.662535Z

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Observation a892b018-b2f4-44b5-97a2-10ef6af099fc · outbound

This paper cites NONLINEAR MECHANICS OF SINGLE-ATOMIC-LAYER GRAPHENE SHEETS.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties NONLINEAR MECHANICS OF SINGLE-ATOMIC-LAYER GRAPHENE SHEETS

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:29.646665Z

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Observation 7211d6eb-d602-46a0-ab4e-5801a17b8d19 · outbound

This paper cites Analysis of phonons in graphene sheets by means of HREELS measurement and ab initio calculation.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Analysis of phonons in graphene sheets by means of HREELS measurement and ab initio calculation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:29.630514Z

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Observation a9349f89-f762-45b4-874d-46cdb102b33f · outbound

This paper cites Thermal Conduction Across Graphene Cross-Linkers.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Thermal Conduction Across Graphene Cross-Linkers

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:29.615137Z

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Observation c9d31c04-ef43-4df3-843c-05ccefd5423d · outbound

This paper cites Raman spectroscopy of graphene-based materials and its applications in related devices.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Raman spectroscopy of graphene-based materials and its applications in related devices

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:29.598792Z

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Observation 3014f967-f3e9-4b56-9040-580c3337e65c · outbound

This paper cites The Raman redshift of graphene impacted by gold nanoparticles.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties The Raman redshift of graphene impacted by gold nanoparticles

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:29.582508Z

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Observation 699c2c86-5feb-4048-9759-98574ce3432d · outbound

This paper cites an unresolved cited work.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:47:29.565310Z

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Observation ed7b0f6b-549a-4d08-b72a-6aaa7281fa5b · outbound

This paper cites , title =.

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties , title =

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T20:47:29.426608Z

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