Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T23:56:54.357673Z
Paper Citation Record · LEDGER
As of 23 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 0 inbound Pith citation observations for arXiv:2506.15652.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T23:56:54.357673Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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
64 of 64 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation d6a8b6b9-d02e-4727-855d-c4b1be2c9cd7 · outbound
A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials J.; Jain, J
Reference 1
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials Unresolved cited work
Reference 3
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials Phonon transport in disordered alloys: A Multiple-scattering approach
Reference 4
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials Dynamic disorder phonon scattering mediated by Cu atomic hopping and diffusion in Cu3SbSe3
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials Thermal conductivity modeling on highly disordered crystalline Y _ 1-x Nb _x O _ 1.5+x : Beyond the phonon scenario
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials Atomic Disorder Enables Superior Catalytic Surface of Pt-Based Catalysts for Alkaline Hydrogen Evolution
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials J.; Dove, M
Reference 10
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Reference 13
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Reference 14
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials Cluster expansion method for multicomponent systems based on optimal selection of structures for density-functional theory calculations
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials Atomic cluster expansion for accurate and transferable interatomic potentials
Reference 16
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials Unresolved cited work
Reference 17
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials M.; Isayev, O
Reference 19
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials Unresolved cited work
Reference 21
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials Graph Attention Networks
Reference 22
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials Unresolved cited work
Reference 23
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials Graph neural networks for materials science and chemistry
Reference 24
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials Leveraging Persistent Homology Features for Accurate Defect Formation Energy Predictions via Graph Neural Networks
Reference 25
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials Unresolved cited work
Reference 26
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials T.; Alatas, A.; Kong, J.; Li, M
Reference 27
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Reference 28
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials M.; Charpagne, M.-A.; Latypov, M
Reference 29
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation
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Reference 31
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Reference 32
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Reference 33
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials The world of two-dimensional carbides and nitrides (MXenes)
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials Chemical Origin of Termination-Functionalized MXenes: Ti3C2 T 2 as a Case Study
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials Database of Tensorial Optical and Transport Properties of Materials From the Wannier Function Method
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Reference 37
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Reference 43
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Reference 44
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials The Rise of MXenes
Reference 45
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Reference 46
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Reference 47
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Reference 48
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Reference 49
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Reference 51
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Reference 55
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Reference 57
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Reference 58
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials Unresolved cited work
Reference 59
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials BoltzWann: A code for the evaluation of thermoelectric and electronic transport properties with a maximally-localized Wannier functions basis
Reference 60
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Reference 61
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Reference 62
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Reference 63
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A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials Algorithms for Hyper-Parameter Optimization
Reference 64
Source-reported events for the cited work
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No inbound Pith citation observations are available.