Paper Citation Record · LEDGER

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors

As of 23 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 2 inbound Pith citation observations for arXiv:2502.09970.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.09970 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T19:56:13.172459Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:40:14.438248Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T22:23:21.085903Z

Reference resolution

43 of 43 outbound references displayed

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External citation measurements

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Outbound references

Observation 045e9d24-8c0c-4fe1-9b11-635772191a8c · outbound

This paper cites Solid -State lithium -ion bat tery electrolytes: Revolutionizing energy density and safety,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Solid -State lithium -ion bat tery electrolytes: Revolutionizing energy density and safety,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T19:56:13.026307Z

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Observation a4d04ba4-1827-4d55-8a22-3fc9b0481002 · outbound

This paper cites Designing solid -state electrolytes for safe, energy -dense batteries,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Designing solid -state electrolytes for safe, energy -dense batteries,

Reference 2

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Observation e6aad4d8-bec0-424e-9729-93b9ac21b9b2 · outbound

This paper cites A solid future for batte ry development,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors A solid future for batte ry development,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T19:56:13.034220Z

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Observation 66e284df-2a37-4d08-aac2-2a3bec9972f8 · outbound

This paper cites Challenges in speeding up solid -state battery development,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Challenges in speeding up solid -state battery development,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T19:56:13.037867Z

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Observation bc435fa6-7a0c-4362-b7b4-ede3b3118a12 · outbound

This paper cites Fundamentals of inorganic solid - state electrolytes for batteries,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Fundamentals of inorganic solid - state electrolytes for batteries,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T19:56:13.041590Z

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Observation ae52cb8e-8a07-4fd2-aa6b-1be5a81a3e6d · outbound

This paper cites Lithium superionic conductors with corner -sharing frameworks,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Lithium superionic conductors with corner -sharing frameworks,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T19:56:13.045098Z

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Observation bd628569-00af-46d2-8f47-9f2415a96194 · outbound

This paper cites A lithium superionic conductor for mil limeter-thick battery electrode,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors A lithium superionic conductor for mil limeter-thick battery electrode,

Reference 7

Resolution: verified exact; doi, observed 2026-08-07T19:56:13.408209Z

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Observation b8bc7ffc-a2f7-4d51-b6dd-e1f363d7e029 · outbound

This paper cites High-Voltage Superionic Halide Solid Electrolytes for All-Solid-State Li-Ion Batteries,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors High-Voltage Superionic Halide Solid Electrolytes for All-Solid-State Li-Ion Batteries,

Reference 8

Resolution: verified exact; doi, observed 2026-08-07T19:56:13.396742Z

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Observation 9e20ea61-aaec-4c9e-92f5-7dbf55da036d · outbound

This paper cites Prospects of halide-based all-solid-state batteries: From material design to practical application,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Prospects of halide-based all-solid-state batteries: From material design to practical application,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T19:56:13.055679Z

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Observation 3bf8278d-0b34-4fe8-a1f3-95d3b409ac09 · outbound

This paper cites Carbon-free high-loading silicon anodes enabled by sulfide solid electrolytes,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Carbon-free high-loading silicon anodes enabled by sulfide solid electrolytes,

Reference 10

Resolution: verified exact; doi, observed 2026-08-07T19:56:13.378211Z

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Observation b5b247d1-c3ea-4a28-9e4a-e75b933051b3 · outbound

This paper cites The General AMBER Force Field (GAFF) Can Accurately Predict Thermodynamic and Transport Properties of Many Ionic Liquids,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors The General AMBER Force Field (GAFF) Can Accurately Predict Thermodynamic and Transport Properties of Many Ionic Liquids,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T19:56:13.062217Z

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Observation b5e14426-ec45-44af-af2f-e8047622a448 · outbound

This paper cites CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T19:56:13.066035Z

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Observation f53c1387-aa73-4d59-aa61-c2767ef3ef8e · outbound

This paper cites Extension of the GROMOS 56a6CARBO/CARBO_R Force Field for Charged, Protonated, and Esterified Uronates,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Extension of the GROMOS 56a6CARBO/CARBO_R Force Field for Charged, Protonated, and Esterified Uronates,

Reference 13

Resolution: verified exact; doi, observed 2026-08-07T19:56:13.352908Z

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Observation ceef1c68-61dd-406b-9530-26b85b7cfa04 · outbound

This paper cites Self-Consistent Equations Including Exchange and Correlation Effects,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Self-Consistent Equations Including Exchange and Correlation Effects,

Reference 14

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Observation bc114e64-fe77-4004-864d-40886d6a23e0 · outbound

This paper cites Anharmonic Molecular Mechanics: Ab Initio Based Morse Parametrizations for the Popular MM3 Force Field,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Anharmonic Molecular Mechanics: Ab Initio Based Morse Parametrizations for the Popular MM3 Force Field,

Reference 15

Resolution: verified exact; doi, observed 2026-08-07T19:56:13.334429Z

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Observation c1264814-ffd5-45aa-8691-a1e0ad02bf97 · outbound

This paper cites Perspective: Machine learning potentials for atomistic simulations,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Perspective: Machine learning potentials for atomistic simulations,

Reference 16

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Observation da91be15-acaf-4ad6-8b5a-4dfe1b65fb91 · outbound

This paper cites Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New Horizons,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New Horizons,

Reference 17

Resolution: verified exact; doi, observed 2026-08-07T19:56:13.315534Z

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Observation 412c4960-823d-4547-a7f1-4a600215916d · outbound

This paper cites Recent advances and applications of machine learning in solid-state materials science,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Recent advances and applications of machine learning in solid-state materials science,

Reference 18

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Observation 5a98fa2f-5ebf-4a90-a3e2-55031a3f641e · outbound

This paper cites Machine Learning Force Fields,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Machine Learning Force Fields,

Reference 19

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Observation 2d1ae2d3-0dc7-49a9-a35d-3fee586a5b07 · outbound

This paper cites Riebesell, R.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Riebesell, R

Reference 20

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Observation 730cd1a6-87ca-4ce4-a83b-1961e5bd8516 · outbound

This paper cites Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields

Reference 21

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Observation ffa67377-1d9d-4d43-a91c-b97a0694e219 · outbound

This paper cites Systematic softening in universal machine learning intera tomic potentials,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Systematic softening in universal machine learning intera tomic potentials,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T19:56:13.098184Z

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Observation 1fcf7ddc-5ecc-4a05-9f56-e27f09a13a30 · outbound

This paper cites MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:56:13.692794Z

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Observation ec28ce5c-6ab1-499d-89f3-c81977b8d18c · outbound

This paper cites Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

Reference 24

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Observation 54335129-ab51-4059-b618-a856bfc40a84 · outbound

This paper cites MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

Reference 25

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Observation c0b58159-91e0-4460-b181-1b439e7ba31d · outbound

This paper cites Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations,

Reference 26

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Observation 2c09d3fa-1ad1-4faa-8ad3-a0227ed6424c · outbound

This paper cites CHGNet as a pretrained universal neural network potential for charge -informed atomistic modelling,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors CHGNet as a pretrained universal neural network potential for charge -informed atomistic modelling,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T19:56:13.117383Z

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Observation 6c3630c4-d0de-4e9a-98d3-e6a6b2b9ef13 · outbound

This paper cites A universal graph deep learning interatomic potential for the periodic table,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors A universal graph deep learning interatomic potential for the periodic table,

Reference 28

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Observation b2b76f11-8754-4a63-80b2-217e158ec763 · outbound

This paper cites Orb: A Fast, Scalable Neural Network Potential.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Orb: A Fast, Scalable Neural Network Potential

Reference 29

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Observation 0e35dc7d-b700-407e-95c2-cffaf110710e · outbound

This paper cites DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-07T19:56:13.537427Z

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Observation 357dc729-4da0-4158-a66d-f3bee9d42306 · outbound

This paper cites Universal Machine Learning Interatomic Potentials are Ready for Phonons,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Universal Machine Learning Interatomic Potentials are Ready for Phonons,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:56:13.682546Z

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This paper cites Neural Message Passing for Quantum Chemistry,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Neural Message Passing for Quantum Chemistry,

Reference 32

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Observation dba76fa4-8df2-4b7a-9968-642b5688d954 · outbound

This paper cites Generalized Neural -Network Representation of High -Dimensional Potential- Energy Surfaces,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Generalized Neural -Network Representation of High -Dimensional Potential- Energy Surfaces,

Reference 33

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Observation 1bc1f633-f47f-47e5-9e23-b3a656abaa33 · outbound

This paper cites E(3) -equivariant graph neural networks for data -efficient and accurate interatomic potentials,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors E(3) -equivariant graph neural networks for data -efficient and accurate interatomic potentials,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T19:56:13.141992Z

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This paper cites The atomic simulation environment -a Python library for working with atoms,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors The atomic simulation environment -a Python library for working with atoms,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:56:13.661287Z

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This paper cites Assessment and optimization of the fast inertial relaxation engine (fire) for energy minimization in atomistic simulations and its implementation in lammps,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Assessment and optimization of the fast inertial relaxation engine (fire) for energy minimization in atomistic simulations and its implementation in lammps,

Reference 36

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This paper cites Commentary: The Materials Project: A materials genome approach to accelerating materials innovation,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Commentary: The Materials Project: A materials genome approach to accelerating materials innovation,

Reference 37

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This paper cites Active learning of uniformly accurate interatomic potentials for materials simulation,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Active learning of uniformly accurate interatomic potentials for materials simulation,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T19:56:13.155050Z

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This paper cites Ab initio molecular dynamics: Concepts, recent developments, and future trends,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Ab initio molecular dynamics: Concepts, recent developments, and future trends,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T19:56:13.158964Z

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This paper cites Projector augmented-wave method,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Projector augmented-wave method,

Reference 40

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Observation b7f85376-8cb9-4749-b653-e7f5bbf0f015 · outbound

This paper cites Robust training of machine learning interat omic potentials with dimensionality reduction and stratified sampling,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Robust training of machine learning interat omic potentials with dimensionality reduction and stratified sampling,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T19:56:13.165841Z

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Observation 62ab6198-4d77-4da2-9a77-45b3d796bb04 · outbound

This paper cites Data-Driven First-Principles Methods for the Study and Design of Alkali Superionic Conductors,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Data-Driven First-Principles Methods for the Study and Design of Alkali Superionic Conductors,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T19:56:13.169255Z

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Observation 33282c94-bf43-4f4d-820a-b794d59125f4 · outbound

This paper cites Accelerating Computational Materials Discovery with Machine Learn ing and Cloud High - Performance Computing: from Large-Scale Screening to Experimental Validation,.

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors Accelerating Computational Materials Discovery with Machine Learn ing and Cloud High - Performance Computing: from Large-Scale Screening to Experimental Validation,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T19:56:13.172459Z

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Pith citing papers

Observation a7ac4799-c016-4131-ad4f-fc6441bdb261 · inbound

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) cites this paper.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:14.438248Z

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Observation 5c5893e1-0883-471b-a35c-1a58407ecbc0 · inbound

Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications cites this paper.

Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:23:21.139496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.