Paper Citation Record · LEDGER

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks

As of 23 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2604.16468.

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

pith.paper-citation-record.v1
2604.16468 v1

Coverage vector

measured 42 of 42 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

42 of 42 outbound references displayed

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Outbound references

Observation eedb0475-a74a-46c7-98a9-136bf8e1da76 · outbound

This paper cites held- 2 out.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks held- 2 out

Reference 1

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This paper cites We then introduce our element -level graph representation and the GATv2 -based multi- label classifier, together with training details and Optuna -based hyperparameter selection.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks We then introduce our element -level graph representation and the GATv2 -based multi- label classifier, together with training details and Optuna -based hyperparameter selection

Reference 2

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This paper cites pure -corner.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks pure -corner

Reference 3

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Observation 8ebf5071-61af-45ae-9964-2cc61f41b2cb · outbound

This paper cites Before evaluating the physics constraints, we first established an optimal baseline model using Bayesian hyperparameter tuning.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks Before evaluating the physics constraints, we first established an optimal baseline model using Bayesian hyperparameter tuning

Reference 4

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Observation 34c22755-dba9-46dc-aa23-1cb34d942706 · outbound

This paper cites A deliberate separation of physics -informed training and post hoc decoding proved crucial.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks A deliberate separation of physics -informed training and post hoc decoding proved crucial

Reference 5

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Observation 952e42ca-5fe6-4ebb-9e35-7689e8976039 · outbound

This paper cites THE CALPHAD METHOD AND ITS ROLE IN MATERIAL AND PROCESS DEVELOPMENT.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks THE CALPHAD METHOD AND ITS ROLE IN MATERIAL AND PROCESS DEVELOPMENT

Reference 6

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Observation 700dbffb-9571-4fcd-b299-f79b1136091a · outbound

This paper cites Physics-consistent machine learning with output projection onto physical manifolds.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks Physics-consistent machine learning with output projection onto physical manifolds

Reference 7

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This paper cites & Tamura, R.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks & Tamura, R

Reference 8

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This paper cites A framework to predict binary liquidus by combining machine learning and CALPHAD assessments.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks A framework to predict binary liquidus by combining machine learning and CALPHAD assessments

Reference 9

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Observation a5d74ae2-d3f5-461a-b8e1-a6e3f4613341 · outbound

This paper cites AIPHAD, an active learning web application for visual understanding of phase diagrams.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks AIPHAD, an active learning web application for visual understanding of phase diagrams

Reference 10

Resolution: verified exact; doi, observed 2026-05-10T18:05:41.942661Z

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Observation 59c7ac19-0805-40f7-a53e-f2a87f43c43f · outbound

This paper cites Pycalphad: Calphad-Based Computational Thermodynamics in Python.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks Pycalphad: Calphad-Based Computational Thermodynamics in Python

Reference 11

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Observation 8518b7cc-04aa-4110-94fd-939d6b632f8a · outbound

This paper cites APL Materials , author =.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks APL Materials , author =

Reference 12

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Observation a3c7868d-6754-44b2-97d4-dcadb48cd543 · outbound

This paper cites Phase diagram construction supported by artificial intelligence.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks Phase diagram construction supported by artificial intelligence

Reference 13

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Observation 799e7fc7-01cd-4645-9746-657747f1f18f · outbound

This paper cites aLLoyM: a large language model for alloy phase diagram prediction.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks aLLoyM: a large language model for alloy phase diagram prediction

Reference 14

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Observation 06691248-433b-4974-b6be-6f88680caa89 · outbound

This paper cites doi: 10.1103/physrevlett.120.145301.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks doi: 10.1103/physrevlett.120.145301

Reference 15

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Observation daca7948-b31d-4083-8e96-0170b04697e3 · outbound

This paper cites Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals.Chemistry of Materials, 31(9):3564–3572, May 2019.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals.Chemistry of Materials, 31(9):3564–3572, May 2019

Reference 16

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Observation 66785f8b-f2f4-4e00-8ca4-ed0601ac92d7 · outbound

This paper cites Atomistic Line Graph Neural Network for improved materials property predictions.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks Atomistic Line Graph Neural Network for improved materials property predictions

Reference 17

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This paper cites Graph convolutional neural networks with global attention for improved materials property prediction.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks Graph convolutional neural networks with global attention for improved materials property prediction

Reference 18

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Observation b958632c-49db-426f-a0fa-a344c57dd761 · outbound

This paper cites In: IEEE Transactions on Knowledge and Data Engineering, vol.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks In: IEEE Transactions on Knowledge and Data Engineering, vol

Reference 19

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Observation 5a2be479-0d5e-4404-b279-985f3126d02e · outbound

This paper cites Recent advances on electromigration in very-large-scale-integration of interconnects.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks Recent advances on electromigration in very-large-scale-integration of interconnects

Reference 20

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Observation 0522bd9c-1e81-4159-9739-c2a1e33e86cd · outbound

This paper cites Accelerating multicomponent phase-coexistence calculations with physics-informed neural networks.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks Accelerating multicomponent phase-coexistence calculations with physics-informed neural networks

Reference 21

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Observation 35c8ac60-f00b-484a-b8b6-3ea460b55600 · outbound

This paper cites NIST Phase Diagrams and Computational Thermodynamics - Solder Systems - SRD 139.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks NIST Phase Diagrams and Computational Thermodynamics - Solder Systems - SRD 139

Reference 22

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Observation d3407387-0f84-4dc8-82b8-611bf9f15a01 · outbound

This paper cites A general-purpose machine learning framework for predicting properties of inorganic materials.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks A general-purpose machine learning framework for predicting properties of inorganic materials

Reference 23

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This paper cites 2018 , issn =.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks 2018 , issn =

Reference 24

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This paper cites Graph Attention Networks.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks Graph Attention Networks

Reference 25

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Observation 29e2df96-4703-400c-9501-535b89c24577 · outbound

This paper cites How Attentive are Graph Attention Networks?.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks How Attentive are Graph Attention Networks?

Reference 26

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Observation a1d6c566-9e06-4eb5-9451-caeb591b0a44 · outbound

This paper cites Decoupled Weight Decay Regularization.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks Decoupled Weight Decay Regularization

Reference 27

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Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 28

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This paper cites Akiba, S.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks Akiba, S

Reference 29

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This paper cites Random Search for Hyper-Parameter Optimization Yoshua Bengio.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks Random Search for Hyper-Parameter Optimization Yoshua Bengio

Reference 30

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Observation 2501f080-1264-485c-8853-26d3767a32c8 · outbound

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Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks Unresolved cited work

Reference 31

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Observation 29912537-58da-417a-9945-906e76256028 · outbound

This paper cites Fries, and Bo Sundman, Computational Thermodynamics : The Calphad Method.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks Fries, and Bo Sundman, Computational Thermodynamics : The Calphad Method

Reference 32

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This paper cites Attention is All you Need.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks Attention is All you Need

Reference 33

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Observation 66b87c04-5992-442d-89bb-ae52536ce86e · outbound

This paper cites Neural Message Passing for Quantum Chemistry.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks Neural Message Passing for Quantum Chemistry

Reference 34

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Observation ca4a1792-ca34-49d6-9ea9-4da3a6d72042 · outbound

This paper cites Dropout: A Simple Way to Prevent Neural Networks from Overfitting.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks Dropout: A Simple Way to Prevent Neural Networks from Overfitting

Reference 35

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This paper cites Adam: A Method for Stochastic Optimization.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks Adam: A Method for Stochastic Optimization

Reference 36

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Observation d5dbfcf4-a1bd-45d5-a4a0-2d277735d2af · outbound

This paper cites Snoek, H.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks Snoek, H

Reference 37

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Observation 1ab6d2f6-b2cf-45c0-882f-9b79f7f471db · outbound

This paper cites Orr and K.-Robert.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks Orr and K.-Robert

Reference 38

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Observation ebf991f5-7b88-4fb1-990d-4a919028a2cc · outbound

This paper cites Gibbs-Equilibrium of Heterogeneous Substances.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks Gibbs-Equilibrium of Heterogeneous Substances

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:14.810536Z

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Observation e0c3715c-fd5b-49ce-b9d2-294602e03401 · outbound

This paper cites Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:14.873002Z

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Observation c24e9250-b488-41c7-99aa-d1583dff761f · outbound

This paper cites OptNet: Differentiable Optimization as a Layer in Neural Networks.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks OptNet: Differentiable Optimization as a Layer in Neural Networks

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:14.841137Z

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Observation 139c7d45-2a2c-499b-bd10-10e75694a9a3 · outbound

This paper cites Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles.

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T07:29:14.813982Z

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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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