Paper Citation Record · LEDGER

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials

As of 16 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2507.05480.

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

pith.paper-citation-record.v1
2507.05480 v1

Coverage vector

measured 46 of 46 reference resolution

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One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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

Source: arxiv_reference, observed 2026-07-03T13:28:19.410136Z

Reference resolution

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External citation measurements

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Outbound references

Observation 6cfbdca4-7c93-40c6-8fa1-94d36563ac4f · outbound

This paper cites Machine learning interatomic potentials as emerging tools for materials science,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Machine learning interatomic potentials as emerging tools for materials science,

Reference 1

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Observation ccf61b75-e675-45cb-ab42-f45f7ad3a458 · outbound

This paper cites Uma: A family of universal models for atoms,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Uma: A family of universal models for atoms,

Reference 2

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Observation e9b69251-e448-4dfa-8967-7dd0575bb9c8 · outbound

This paper cites Deep-learning density functional perturbation theory,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Deep-learning density functional perturbation theory,

Reference 3

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This paper cites Graph trans- former networks for accurate band structure prediction: An end-to-end approach,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Graph trans- former networks for accurate band structure prediction: An end-to-end approach,

Reference 4

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Observation 10f756b5-693a-4be0-8f2f-68a3d0ee8e81 · outbound

This paper cites Transferable equivariant graph neural networks for the hamiltonians of molecules and solids,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Transferable equivariant graph neural networks for the hamiltonians of molecules and solids,

Reference 5

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Observation 0aee6a3a-48ed-4799-8b16-9b2252b8d0db · outbound

This paper cites Time-reversal equivariant neural network potential and hamiltonian for magnetic materials,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Time-reversal equivariant neural network potential and hamiltonian for magnetic materials,

Reference 6

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Observation 08a77b93-3640-4670-a863-00598d6e3818 · outbound

This paper cites Transferable E(3) equivariant parameterization for Hamiltonian of molecules and solids.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Transferable E(3) equivariant parameterization for Hamiltonian of molecules and solids

Reference 7

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Observation be52ae38-0c09-41ea-8416-d406bbbecb72 · outbound

This paper cites A generative model for inor- ganic materials design,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials A generative model for inor- ganic materials design,

Reference 8

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Observation 206075c3-feef-4c16-bd60-77094d1870d5 · outbound

This paper cites Fine-Tuned Language Models Generate Stable Inorganic Materials as Text.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Reference 9

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Observation 78435836-cbb3-4abf-832d-473f139a797a · outbound

This paper cites General frame- work for e (3)-equivariant neural network representation of density functional theory hamiltonian,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials General frame- work for e (3)-equivariant neural network representation of density functional theory hamiltonian,

Reference 10

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Observation 8255040e-ed8d-465b-a6bd-d5890dbfc1a9 · outbound

This paper cites Universal materials model of deep-learning density functional theory hamiltonian,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Universal materials model of deep-learning density functional theory hamiltonian,

Reference 11

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Observation 2759419c-1f50-4166-af7b-c376e02d3aba · outbound

This paper cites Accelerating the calculation of electron-phonon coupling by machine learning methods.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Accelerating the calculation of electron-phonon coupling by machine learning methods

Reference 12

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Observation d0e85484-16d5-45a5-a574-f22efd3fd4ec · outbound

This paper cites Electronic excitations: density-functional versus many-body green’s-function approaches,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Electronic excitations: density-functional versus many-body green’s-function approaches,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:32:35.413592Z

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Observation 326195a1-be15-443e-b4f9-aef99d13f581 · outbound

This paper cites Electron correlation in semiconductors and insulators: Band gaps and quasiparticle energies,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Electron correlation in semiconductors and insulators: Band gaps and quasiparticle energies,

Reference 14

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Observation 5291f692-a16d-4fc8-a865-a65b4f974e65 · outbound

This paper cites The computational complexity of density functional theory,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials The computational complexity of density functional theory,

Reference 16

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Observation 486475b0-2425-4ca3-874d-ae72cc571d2d · outbound

This paper cites Computational complexity of interacting electrons and fun- damental limitations of density functional theory,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Computational complexity of interacting electrons and fun- damental limitations of density functional theory,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:32:35.374668Z

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Observation 9a571289-3167-4172-9127-82df8a980bd7 · outbound

This paper cites Electron-hole excitations and optical spectra from first principles,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Electron-hole excitations and optical spectra from first principles,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:32:35.394302Z

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This paper cites Coupled-cluster theory in quantum chemistry,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Coupled-cluster theory in quantum chemistry,

Reference 19

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Observation 899a83b1-7c2a-44a2-bc51-2ea7ef1823ec · outbound

This paper cites Nearsightedness of electronic matter,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Nearsightedness of electronic matter,

Reference 20

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Observation 20ba82c3-ec7b-45b5-b5cf-b24ada7de1dd · outbound

This paper cites Deep-learning density functional theory hamiltonian for efficient ab initio electronic-structure calculation,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Deep-learning density functional theory hamiltonian for efficient ab initio electronic-structure calculation,

Reference 21

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MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Unresolved cited work

Reference 22

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Observation 736738c3-8310-40a8-a8fc-df441aeb4254 · outbound

This paper cites Representing individual electronic states for machine learning gw band structures of 2d materials,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Representing individual electronic states for machine learning gw band structures of 2d materials,

Reference 23

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Observation 396be13b-cd1b-4c37-9686-4aceb27920f1 · outbound

This paper cites Spectral Operator Representations.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Spectral Operator Representations

Reference 24

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Observation 21b31a87-83d3-43c0-8076-cc096789926f · outbound

This paper cites Unified Deep Learning Framework for Many-Body Quantum Chemistry via Green's Functions.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Unified Deep Learning Framework for Many-Body Quantum Chemistry via Green's Functions

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:32:29.648015Z

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Observation c48f9df9-f02b-41c0-8701-032ec46588ae · outbound

This paper cites Data-driven Low-rank Approximation for Electron-hole Kernel and Acceleration of Time-dependent GW Calculations.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Data-driven Low-rank Approximation for Electron-hole Kernel and Acceleration of Time-dependent GW Calculations

Reference 26

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Observation ccc5ff99-b3e4-4695-b11d-6fd0678a28d9 · outbound

This paper cites Attention is all you need,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Attention is all you need,

Reference 27

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Observation adad06d1-173e-4311-8432-18453f79a97e · outbound

This paper cites Recent progress of the computational 2d materials database (c2db),.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Recent progress of the computational 2d materials database (c2db),

Reference 28

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Observation 6824469c-3b30-4954-91c8-784228a5bab2 · outbound

This paper cites The computational 2d materials database: high-throughput modeling and discovery of atomically thin crystals,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials The computational 2d materials database: high-throughput modeling and discovery of atomically thin crystals,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:32:35.213625Z

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Observation 137b5640-38f3-4f27-bbbb-e2e9055698a7 · outbound

This paper cites Electron–electron and electron-hole interactions in small semiconductor crystallites: The size dependence of the lowest excited electronic state,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Electron–electron and electron-hole interactions in small semiconductor crystallites: The size dependence of the lowest excited electronic state,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:32:35.067203Z

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Observation 0f908fbc-3c98-424c-8e05-1d12283d42b0 · outbound

This paper cites Optical absorption of insulators and the electron-hole interaction: An ab initio calculation,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Optical absorption of insulators and the electron-hole interaction: An ab initio calculation,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:32:34.913161Z

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Observation 5f8cfaec-a8d8-45d4-8965-56d1d017f3fe · outbound

This paper cites Electron-hole excitations in semiconductors and insulators,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Electron-hole excitations in semiconductors and insulators,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:32:34.776151Z

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Observation 151ed672-9a27-48e9-960e-db0f0e49c49a · outbound

This paper cites Hot charge-transfer exci- tons set the time limit for charge separation at donor/acceptor interfaces in organic photovoltaics,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Hot charge-transfer exci- tons set the time limit for charge separation at donor/acceptor interfaces in organic photovoltaics,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:32:34.647057Z

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Observation ccff5676-a3a1-4097-8868-dca270e94f1a · outbound

This paper cites Role of microstructure in the electron–hole interaction of hybrid lead halide perovskites,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Role of microstructure in the electron–hole interaction of hybrid lead halide perovskites,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:32:34.513710Z

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Observation 61502a82-117b-4903-af4e-f626639f0c6b · outbound

This paper cites New method for calculating the one-particle green’s function with application to the electron-gas problem,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials New method for calculating the one-particle green’s function with application to the electron-gas problem,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:32:34.189370Z

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Observation f579612e-d9a9-43ce-8666-3381a24794f2 · outbound

This paper cites Effects of electron-electron and electron-phonon interactions on the one-electron states of solids,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Effects of electron-electron and electron-phonon interactions on the one-electron states of solids,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:32:33.967902Z

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Observation c52848f1-46fd-4691-b2a1-b48ec30fe00e · outbound

This paper cites Quantum theory of the dielectric constant in real solids,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Quantum theory of the dielectric constant in real solids,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:32:33.804915Z

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Observation e4023137-1d26-4ce4-b5f5-4b4cb3afe10d · outbound

This paper cites Dielectric constant with local field effects included,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Dielectric constant with local field effects included,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:32:33.636271Z

Source-reported events for the cited work

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Observation 585d61ec-c0d7-4ee4-a3da-a4357a27f465 · outbound

This paper cites General e (2)-equivariant steerable cnns,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials General e (2)-equivariant steerable cnns,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:32:33.267565Z

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Observation e6abffd6-30af-4e5c-a08a-bf2afc899bb6 · outbound

This paper cites Unsupervised representation learning of kohn–sham states and consequences for downstream predictions of many-body effects,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Unsupervised representation learning of kohn–sham states and consequences for downstream predictions of many-body effects,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:32:32.835654Z

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Observation 4c091a14-4332-4324-92c9-b6137b534a77 · outbound

This paper cites General E(2)-Equivariant Steerable CNNs,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials General E(2)-Equivariant Steerable CNNs,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:32:32.490029Z

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Observation a1f15c78-1388-4b95-83e2-78153be681b3 · outbound

This paper cites Atomic positional embedding-based transformer model for predicting the density of states of crys- talline materials,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Atomic positional embedding-based transformer model for predicting the density of states of crys- talline materials,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:32:32.091819Z

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Observation 590e1a3f-ea44-4289-be20-45a9979d850d · outbound

This paper cites Optical spectrum of mos 2: many-body effects and diversity of exciton states,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Optical spectrum of mos 2: many-body effects and diversity of exciton states,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:32:31.776677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 99febfb8-46c0-4f83-be71-ae587f27adcf · outbound

This paper cites Optical spectrum ofmos2: Many-body effects and diversity of exciton states,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Optical spectrum ofmos2: Many-body effects and diversity of exciton states,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:32:31.575245Z

Source-reported events for the cited work

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Observation 78d71aa2-9456-4a35-9267-887c375fbe77 · outbound

This paper cites Berkeleygw: A massively parallel computer package for the calculation of the quasiparticle and optical properties of materials and nanostructures,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Berkeleygw: A massively parallel computer package for the calculation of the quasiparticle and optical properties of materials and nanostructures,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:32:31.263017Z

Source-reported events for the cited work

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Observation 106f6bfd-cd99-4ac0-a0b9-47858ff982ae · outbound

This paper cites Univer- sal ensemble-embedding graph neural network for direct prediction of optical spectra from crystal structures,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Univer- sal ensemble-embedding graph neural network for direct prediction of optical spectra from crystal structures,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:32:31.053125Z

Source-reported events for the cited work

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Observation c83a940d-8cba-41cb-aabf-fa9f19d424b1 · outbound

This paper cites Screening and many-body effects in two-dimensional crystals: Monolayer mos 2,.

MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials Screening and many-body effects in two-dimensional crystals: Monolayer mos 2,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:32:30.788149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation 11d6a0af-c02f-4431-b6f8-1c0dc68ee331 · inbound

Transferable Machine Learning of Electronic Hamiltonians with Superposition-of-Atomic-Potentials Features cites this paper.

Transferable Machine Learning of Electronic Hamiltonians with Superposition-of-Atomic-Potentials Features MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-03T13:28:19.411644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.