Paper Citation Record · LEDGER

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials

As of 23 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2506.12877.

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pith.paper-citation-record.v1
2506.12877 v3

Coverage vector

measured 34 of 34 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

34 of 34 outbound references displayed

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External citation measurements

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Outbound references

Observation a394b548-4ea3-4c15-929c-036f4e58ffdc · outbound

This paper cites Elsevier, 2023.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Elsevier, 2023

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.812840Z

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Observation 0316b4fa-7b8c-4dfc-8e66-f4914d424034 · outbound

This paper cites Oxford university press, 2017.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Oxford university press, 2017

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T00:43:13.273192Z

Source-reported events for the cited work

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Observation a41ddd45-6b3e-4acb-9b8a-ca3b22408e25 · outbound

This paper cites Complexity in strongly correlated elec- tronic systems.Science, 309(5732):257–262, 2005.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Complexity in strongly correlated elec- tronic systems.Science, 309(5732):257–262, 2005

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.795708Z

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Observation a3f41487-29b0-40d6-9ee4-dc39be0be68b · outbound

This paper cites Interface physics in complex oxide heterostructures.Annu.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Interface physics in complex oxide heterostructures.Annu

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.785124Z

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Observation e3619a2d-9433-4284-ad88-623d155c8b11 · outbound

This paper cites Orbital physics in transition-metal oxides.science, 288(5465):462–468, 2000.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Orbital physics in transition-metal oxides.science, 288(5465):462–468, 2000

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.772158Z

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Observation 0f10544e-1b6a-4373-be39-0c7113f297ab · outbound

This paper cites Ferroelastic materials.Annual Review of Materials Research, 42(1):265–283, 2012.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Ferroelastic materials.Annual Review of Materials Research, 42(1):265–283, 2012

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.759484Z

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Observation a26158df-5e37-41c5-9605-597bef2f9bb1 · outbound

This paper cites The renaissance of magnetoelectric multiferroics.Science, 309(5733):391– 392, 2005.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials The renaissance of magnetoelectric multiferroics.Science, 309(5733):391– 392, 2005

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.747572Z

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Observation 82c271eb-11c2-4b1f-b86f-34e4786918e8 · outbound

This paper cites Classifying multiferroics: Mechanisms and effects.Physics, 2:20, 2009.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Classifying multiferroics: Mechanisms and effects.Physics, 2:20, 2009

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.734546Z

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Observation 904abd14-f2a3-4ce5-9015-9c2e6b6d24d3 · outbound

This paper cites A thermodynamic explanation of the invar effect.Nature Physics, 19(11):1642–1648, 2023.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials A thermodynamic explanation of the invar effect.Nature Physics, 19(11):1642–1648, 2023

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.722216Z

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Observation 7e5b8f42-5cde-4428-979f-abb9375aa2da · outbound

This paper cites Origin of the invar effect in iron–nickel alloys.Nature, 400(6739):46–49, 1999.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Origin of the invar effect in iron–nickel alloys.Nature, 400(6739):46–49, 1999

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.710942Z

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Observation 940f8165-3405-4d25-adc3-43a92c36b4b6 · outbound

This paper cites Iron-based superelastic alloys with near-constant critical stress temperature dependence.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Iron-based superelastic alloys with near-constant critical stress temperature dependence

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.698980Z

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Observation 3627bbfa-e274-431f-8611-9d2f2da2aaaf · outbound

This paper cites Structurally triggered metal-insulator transi- tion in rare-earth nickelates.Nature communications, 8(1):1677, 2017.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Structurally triggered metal-insulator transi- tion in rare-earth nickelates.Nature communications, 8(1):1677, 2017

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.685415Z

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Observation d6a814dd-2114-4e39-b24b-c0fb3221f8c9 · outbound

This paper cites Quan- tifying the role of the lattice in metal–insulator phase transitions.Communications Physics, 5(1):135, 2022.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Quan- tifying the role of the lattice in metal–insulator phase transitions.Communications Physics, 5(1):135, 2022

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.672462Z

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Observation 64caedc2-b164-4c47-8da2-53a5c61ed5c7 · outbound

This paper cites Interatomic potentials: Achievements and challenges.Advances in Physics: X, 8(1):2093129, 2023.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Interatomic potentials: Achievements and challenges.Advances in Physics: X, 8(1):2093129, 2023

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.660411Z

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Observation c4fec168-75b9-4ef6-9766-a48ae28a8670 · outbound

This paper cites Enhanced sampling methods for molecular dynamics simulations.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Enhanced sampling methods for molecular dynamics simulations

Reference 15

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Observation efaa3b65-4fa9-499e-96d7-0d9e2b4cfc0b · outbound

This paper cites Deep learning illuminates spin and lattice interaction in magnetic materials.Phys- ical Review B, 110(6):064427, 2024.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Deep learning illuminates spin and lattice interaction in magnetic materials.Phys- ical Review B, 110(6):064427, 2024

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.647095Z

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Observation 4c092b73-1e03-4230-8cfb-219751e48068 · outbound

This paper cites Equivariant neural network force fields for magnetic materials.Quantum Frontiers, 3(1):8, 2024.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Equivariant neural network force fields for magnetic materials.Quantum Frontiers, 3(1):8, 2024

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.633315Z

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Observation c45b5669-ab85-445e-955e-19afdb1a7bc4 · outbound

This paper cites Spin- dependent graph neural network potential for magnetic materials.Physical Review B, 109(14):144426, 2024.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Spin- dependent graph neural network potential for magnetic materials.Physical Review B, 109(14):144426, 2024

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T00:43:13.325545Z

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Unavailable: canonical work link unavailable.

Observation 4e4dfb24-233c-41b9-a0c8-5c6a8b1c8f89 · outbound

This paper cites Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics.Physical review letters, 120(14):143001, 2018.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics.Physical review letters, 120(14):143001, 2018

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.612738Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 182718d0-221f-4794-b23a-63f7a8a05cb3 · outbound

This paper cites Recent advances and outstanding challenges for machine learning interatomic potentials.Nature Computational Science, 3(12):998– 1000, 2023.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Recent advances and outstanding challenges for machine learning interatomic potentials.Nature Computational Science, 3(12):998– 1000, 2023

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.600398Z

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Observation fa4bd8ae-b51a-4e51-8235-faa2a87ae2bb · outbound

This paper cites Algorithm for molecular dynamics simulations of spin liquids.Physical review letters, 86(5):898, 2001.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Algorithm for molecular dynamics simulations of spin liquids.Physical review letters, 86(5):898, 2001

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.587641Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1e61d7f0-32bc-44c8-a4eb-96ed7b7a6334 · outbound

This paper cites Massively parallel symplectic algorithm for coupled magnetic spin dynamics and molecular dynamics.Journal of Computational Physics, 372:406–425, 2018.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Massively parallel symplectic algorithm for coupled magnetic spin dynamics and molecular dynamics.Journal of Computational Physics, 372:406–425, 2018

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.574348Z

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Observation 80055c54-b6b9-45b0-ab04-7828dcaac704 · outbound

This paper cites General theory of fractal path integrals with applications to many-body theories and statistical physics.Journal of mathematical physics, 32(2):400–407, 1991.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials General theory of fractal path integrals with applications to many-body theories and statistical physics.Journal of mathematical physics, 32(2):400–407, 1991

Reference 23

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Observation 3d8b0928-dd95-436e-ad40-191f99a1cfec · outbound

This paper cites Explicit reversible integra- tors for extended systems dynamics.Molecular Physics, 87(5):1117–1157, 1996.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Explicit reversible integra- tors for extended systems dynamics.Molecular Physics, 87(5):1117–1157, 1996

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.552334Z

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Observation 44fcac37-5010-4d0a-b751-ac4780b422f7 · outbound

This paper cites Springer, 1985.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Springer, 1985

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.538687Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b3c55eda-e116-452d-88b8-6b0f0ade50ec · outbound

This paper cites Non-collinear magnetic atomic cluster expansion for iron.npj Computational Materials, 10(1):12, 2024.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Non-collinear magnetic atomic cluster expansion for iron.npj Computational Materials, 10(1):12, 2024

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T00:43:13.350810Z

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Unavailable: canonical work link unavailable.

Observation d57c537b-eb73-4d86-adc7-cbb97361ae45 · outbound

This paper cites Perfor- mance and cost assessment of machine learning inter- atomic potentials.The Journal of Physical Chemistry A, 124(4):731–745, 2020.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Perfor- mance and cost assessment of machine learning inter- atomic potentials.The Journal of Physical Chemistry A, 124(4):731–745, 2020

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.512519Z

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Observation 8dffb5d2-81c5-4b1a-a458-3d4e82a8a7b2 · outbound

This paper cites Nos´ e–hoover chains: The canonical ensemble via continuous dynamics.The Journal of chemical physics, 97(4):2635–2643, 1992.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Nos´ e–hoover chains: The canonical ensemble via continuous dynamics.The Journal of chemical physics, 97(4):2635–2643, 1992

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.500133Z

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Observation e6e285c8-b7bc-4c13-8692-0064d096c004 · outbound

This paper cites Constant pressure molecular dynamics algorithms.J.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Constant pressure molecular dynamics algorithms.J

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T00:43:13.360064Z

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Observation e61229e1-da42-4bbe-bc79-a79dc718fadf · outbound

This paper cites General method for atomistic spin-lattice dynamics with first-principles accuracy.Physical Review 6 B, 99(10):104302, 2019.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials General method for atomistic spin-lattice dynamics with first-principles accuracy.Physical Review 6 B, 99(10):104302, 2019

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.479814Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3400a44e-246f-4a16-b97f-00b273b75d44 · outbound

This paper cites Lammps-a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales.Computer Physics Commu- nications, 271:108171, 2022.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Lammps-a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales.Computer Physics Commu- nications, 271:108171, 2022

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.466603Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f54d755c-595e-46de-a91d-0f51dd9d7263 · outbound

This paper cites Dp-gen: A con- current learning platform for the generation of reliable deep learning based potential energy models.Computer Physics Communications, 253:107206, 2020.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials Dp-gen: A con- current learning platform for the generation of reliable deep learning based potential energy models.Computer Physics Communications, 253:107206, 2020

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.453907Z

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Observation c54a72ee-2cf0-4cf9-9f36-c28a2c5206c0 · outbound

This paper cites On the theory of the dispersion of magnetic permeability in ferromagnetic bodies.Phys.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials On the theory of the dispersion of magnetic permeability in ferromagnetic bodies.Phys

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.440537Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 23f2f8f1-93ff-4bb8-ba7a-5235e3038910 · outbound

This paper cites A phenomenological theory of damp- ing in ferromagnetic materials.IEEE transactions on magnetics, 40(6):3443–3449, 2004.

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials A phenomenological theory of damp- ing in ferromagnetic materials.IEEE transactions on magnetics, 40(6):3443–3449, 2004

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:43:13.425838Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

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