Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability | Paper Citation Record

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This paper cites F., Florea, L., De Oliveira, M.

Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability F., Florea, L., De Oliveira, M

Reference 1

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Unresolved cited work

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability & Buehler, M

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability & Rignanese, G

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability & Gao, F

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Unresolved cited work

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Unresolved cited work

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This paper cites A., Brolo, A.

Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability A., Brolo, A

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Unresolved cited work

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability & Ko, D.-H

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Unresolved cited work

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability MatBench Leaderboard

Reference 12

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Unresolved cited work

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This paper cites P., Kondor, R.

Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability P., Kondor, R

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Unresolved cited work

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability TabNet: Attentive Interpretable Tabular Learning

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This paper cites Connectivity Optimized Nested Graph Networks for Crystal Structures.

Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Connectivity Optimized Nested Graph Networks for Crystal Structures

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This paper cites Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry.

Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Unresolved cited work

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Orb-v3: atomistic simulation at scale

Reference 21

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability & Mizukami, W

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Unresolved cited work

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability M., De Castro, S., Morton, B

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Unresolved cited work

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Leveraging neural network interatomic potentials for a foundation model of chemistry

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability L., Buonassisi, T

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Machine learning in materials science: From explai nable predictions to autonomous design

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability & Kumacheva, E

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Unresolved cited work

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability & Tavazza, F

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Unresolved cited work

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability & Aihara Jr., T

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability & Wolverton, C

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Unresolved cited work

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

Reference 44

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Materialsproject.org https://matbench.materialsproject.org/Full%20Benchmark%20Data/matbench_v0.1_Meg Net_kgcnn_v2.1.0/ (2020)

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability & Qian, Q

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Boosting SISSO Performance on Small Sample Datasets by Using Random Forests Prescreening for Complex Feature Selection

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Unresolved cited work

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability & Guestrin, C

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability & Ong, S

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability TMetalFraction|transition metal fraction

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Unresolved cited work

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability & Ong, S

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability & Jain, A

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability & Ghiringhelli, L

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Mechanical properties of some steels

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability & Tavazza, F

Reference 59

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Unresolved cited work

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability & Brgoch, J

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability & Aihara Jr., T

Reference 62

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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability & Wolverton, C

Reference 63

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4810210c-a44d-4dbb-92ae-ce584881be63 · outbound

This paper cites an unresolved cited work.

Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:43:26.342770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e9ba03be-0c5d-4588-a426-6f0b3a04a8f7 · outbound

This paper cites an unresolved cited work.

Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:43:25.967858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 716eaa0d-c945-4d66-b42e-17bb2dc247db · outbound

This paper cites an unresolved cited work.

Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:43:25.314119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9ebed9bf-9977-4e68-840f-ae7441c6b74c · outbound

This paper cites an unresolved cited work.

Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:43:25.296203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2b7f122a-b49e-4732-bafa-96365b9659a4 · outbound

This paper cites an unresolved cited work.

Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Unresolved cited work

Reference 68

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:43:25.274300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation eb61df4d-d283-46aa-b525-0f0ae1b815c8 · outbound

This paper cites an unresolved cited work.

Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Unresolved cited work

Reference 69

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:43:25.250320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers