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

Observation 99581303-a4cc-450f-a4f7-145cc303a4b1 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:30.653358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cb7cfa75-009d-4ee4-b888-63d1fdf5e5f2 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:30.633017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1c8910bd-4b3c-4549-bd53-f7225be498a4 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:30.604262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3aa87734-6ae3-4f02-bdd8-e8a2545cd592 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:30.580722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 525d3f3d-0c9e-48c7-8b18-f704f53ae760 · 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 5

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:47:30.563617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation dc05b190-4b3f-4d63-8915-edc4b8a4b9fa · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:30.543434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5d7afb2b-b4c3-4caa-b2e6-5ed5113ce18e · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-15T20:47:29.178762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8fc74bb2-eca9-4d5e-a224-6373f09fb514 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:30.510862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation af6cb6c3-b4af-4ffa-88fe-31fe32722fdb · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:30.493721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a6200aa5-8ffe-470f-a853-e18e76811863 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:30.473350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 56d49eb9-449c-4c78-8be7-80f8f79debb3 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:30.455940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation db9e73a4-0963-4ee4-b242-d37d4e970f54 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:30.436395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0dab0972-290e-47ea-abdc-b21a56370c0a · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:30.417151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3cf5add4-c132-47d2-a2b6-2571c8465022 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:30.395352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7574c4d7-b039-49a1-a8e0-377913168cb9 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:30.376210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1b8fd7af-9e7a-41d6-a943-0de1fd2f9b5f · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:30.225417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 20310286-62a3-4b6a-99f9-2dd6c0683d54 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:30.209755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 15199f25-d463-4e44-9489-bdce26697e24 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:30.191501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e134a44f-27ab-46d4-b92b-c730dd6acfe1 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-15T20:47:29.239604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72675e3c-6cd0-41cb-9762-8e99a935be30 · 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 20

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:47:30.163340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 67777ce6-ff7e-436e-a224-888908e98e15 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:30.147555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1b6944d9-5495-4ab3-946e-0cf2d77149dc · outbound

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\

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:30.132659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 46033e0b-6a2d-4472-ab60-6b5c6c5f3cfa · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:30.116882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c30e94e6-6d35-449e-83f2-6bf60d22e78a · 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 24

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:47:30.101346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 89f62b83-3c7e-4bc1-9625-0c7b77345dc7 · 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 25

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:47:30.085633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3b584537-2a86-40b2-8d54-c8cb3804f2ff · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:30.070859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9e248af4-e4b7-4584-b6c3-1b1f82738dfa · outbound

This paper cites Hoover NPT dynamics for systems varying in shape and size.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:30.056650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d273ebc1-f91d-4899-9977-e8280540f9cb · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:30.040807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f0c7965e-6c57-4fac-bbc2-0c6e95cf4443 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:30.022462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3b5d6f77-9922-494e-beb9-081f0e762356 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:30.005642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e259003f-7280-4458-822f-3caa96b17209 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:29.990131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4c69e310-7176-4214-8353-59348d5b0ecb · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:29.975157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c6d3e60e-6395-45b5-9beb-a7c808addb20 · outbound

This paper cites Methodology and meaning of computing heat flux via atomic stress in systems with constraint dynamics.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:29.960026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f0dd0012-88e4-4871-a813-d04c2064e7ff · outbound

This paper cites Implementation strategies in phonopy and phono3py.

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

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T20:47:29.310328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cbccf84d-dd6b-4f8a-8621-de2c694ec725 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:29.933943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 52540fe6-c2fb-471e-9534-a435a58e74dd · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:29.919664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation eb542105-f122-4194-be01-94a82c3dde7e · outbound

This paper cites Velocity-autocorrelation spectrum of simple classical liquids.

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

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:29.903137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ad9469ac-ac2f-4777-bdd5-8dbafba16c63 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:29.887507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6544ad9c-39aa-4393-a35d-f45908b654a8 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:29.872358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a0057bef-28c0-405d-a49c-f2a2bf0cfdec · 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 40

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:47:29.857522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3b6abb4b-f2ba-4e7f-a8ea-a20ad897290c · outbound

This paper cites R.; Millman, K.

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

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:29.842523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ca06bba2-2b3b-4029-95c5-36560383fed0 · 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 42

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:47:29.828008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f2a889a6-c747-4e80-9eb3-4babe7463267 · 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 43

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:47:29.811568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 76f4dc97-62a1-4fc2-b359-2673c190129d · outbound

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1c145e84-182f-40df-b79c-87becfaee4ab · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:29.779911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 498431eb-9596-4175-b9e0-6e129e1d95ef · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:47:29.764668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 094298ef-5672-4fec-b52a-7f4b51b365d4 · outbound

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bf8b4b53-2fe4-4996-82a8-503cb706afdb · outbound

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation df6e24e7-f9c2-40f4-b266-e8f684e44cb2 · outbound

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers