Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-06-28T17:30:28.329639Z
Paper Citation Record · LEDGER
Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach
As of 8 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2606.01012.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
16 of 16 outbound references displayed
- verified exact1
- verified fuzzy0
- unresolved14
- parse uncertain0
- malformed identifier0
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation b9cb8868-ebf5-4fa7-8227-7e6ff2d53974 · outbound
This paper cites High throughput calculations for a dataset of bilayer materials.Scientific Data, 10(1):232,.
Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach High throughput calculations for a dataset of bilayer materials.Scientific Data, 10(1):232,
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T17:30:28.329639Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e13afd4b-5595-4cd7-9814-dc91a9e2c950 · outbound
This paper cites Graph networks as a universal machine learning framework for molecules and crystals.
Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach Graph networks as a universal machine learning framework for molecules and crystals
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T17:30:28.329639Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c87fa9d7-d4c4-4bda-b6e7-608376595318 · outbound
This paper cites Structural embedding methods for machine learning models accelerate research on stacked 2d materials.The Journal of Physical Chemistry C, 128(37):15512–15521,.
Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach Structural embedding methods for machine learning models accelerate research on stacked 2d materials.The Journal of Physical Chemistry C, 128(37):15512–15521,
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T17:30:28.329639Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1cabe7ca-9db1-4840-a895-81ba7f7c6304 · outbound
This paper cites Atomistic line graph neural network for improved materials property predictions.npj Computational Materi- als, 7(1):185,.
Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach Atomistic line graph neural network for improved materials property predictions.npj Computational Materi- als, 7(1):185,
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T17:30:28.329639Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f86033a0-fbec-4314-a40f-30946d57a13d · outbound
This paper cites an unresolved cited work.
Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach Unresolved cited work
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T17:30:28.329639Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8c58f4e4-d3b3-441a-ad37-e6864d1e1867 · outbound
This paper cites Efficient iterative schemes for ab initio total- energy calculations using a plane-wave basis set.Physical review B, 54(16):11169,.
Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach Efficient iterative schemes for ab initio total- energy calculations using a plane-wave basis set.Physical review B, 54(16):11169,
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T17:30:28.329639Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a318c30e-ab50-43fd-ae5c-7577091d756e · outbound
This paper cites Observation of van hove singularities in twisted graphene layers.Nature physics, 6(2):109–113,.
Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach Observation of van hove singularities in twisted graphene layers.Nature physics, 6(2):109–113,
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T17:30:28.329639Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6be9e63c-012b-4c79-b651-6240877c2f8b · outbound
This paper cites Scaling deep learning for materials discovery.Nature, 624(7990):80–85,.
Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach Scaling deep learning for materials discovery.Nature, 624(7990):80–85,
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T17:30:28.329639Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 97baa8e5-dfc0-44a7-8c2e-eeeea58ee1a5 · outbound
This paper cites Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds.Nature nanotechnol- ogy, 13(3):246–252,.
Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds.Nature nanotechnol- ogy, 13(3):246–252,
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T17:30:28.329639Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c55cd0b5-58d8-46f0-8951-9c80e1b4e647 · outbound
This paper cites High-throughput computational stacking reveals emergent properties in natural van der waals bilay- ers.Nature Communications, 15(1):932,.
Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach High-throughput computational stacking reveals emergent properties in natural van der waals bilay- ers.Nature Communications, 15(1):932,
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T17:30:28.329639Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b4ec90ce-0c4a-4198-99ee-0152f23507c9 · outbound
This paper cites an unresolved cited work.
Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach Unresolved cited work
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T17:30:28.329639Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b3f2e8c3-58e0-4a8c-a34a-95f8f504773a · outbound
This paper cites Equivariant message passing for the prediction of tensorial properties and molecular spectra.
Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach Equivariant message passing for the prediction of tensorial properties and molecular spectra
Reference 12
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-01T21:06:13.651127Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 38ef3b8f-fd53-4f9f-9abe-69019a8b3452 · outbound
This paper cites A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools.
Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T21:06:13.648123Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 8ed0e42f-5101-474a-96a7-9daf021a5314 · outbound
This paper cites Stacking-engineered heterostructures in transition metal dichalcogenides.Advanced Materials, 33(16):2005735,.
Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach Stacking-engineered heterostructures in transition metal dichalcogenides.Advanced Materials, 33(16):2005735,
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T17:30:28.329639Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a9d7b3e9-8bf8-48c6-b384-2c9b1ab2f565 · outbound
This paper cites Pyhtstack2d: A python package for high-throughput homo/hetero stacking of 2d materials.Computer Physics Communications, 312:109618,.
Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach Pyhtstack2d: A python package for high-throughput homo/hetero stacking of 2d materials.Computer Physics Communications, 312:109618,
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T17:30:28.329639Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0a4e0320-e291-4a28-9a03-39a149f008ce · outbound
This paper cites 2dmatpedia, an open computational database of two-dimensional materials from top-down and bottom-up approaches.Scientific data, 6(1):86, 2019.
Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach 2dmatpedia, an open computational database of two-dimensional materials from top-down and bottom-up approaches.Scientific data, 6(1):86, 2019
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-06-28T17:30:28.329639Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.