Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T21:52:46.910853Z
Paper Citation Record · LEDGER
As of 20 August 2026, this Paper Citation Record lists 71 of 71 outbound references and 0 inbound Pith citation observations for arXiv:2505.08732.
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 2026-08-15T21:52:46.910853Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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
71 of 71 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation b58857ff-6575-4144-b328-17aeaf01dcae · outbound
Reference 1
Source-reported events for the cited work
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Observation c399eb6a-33a2-4a74-8c1d-ecf49b28d7db · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Direct observation of twinning in tin lamellae
Reference 2
Source-reported events for the cited work
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Observation 009817c3-4773-42b0-9b94-1c909cbe07f6 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Christian, The Theory of Transformations in Metals and Alloys , (Newnes, 2002)
Reference 3
Source-reported events for the cited work
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Observation 273a38c9-1ea3-4736-92a2-972ae3c2da07 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Deformation behavior of tin and some tin alloys
Reference 4
Source-reported events for the cited work
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Observation 0307b606-8986-4ca3-a1ac-aa9467d6ba99 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Cryogenic in situ microcompression testing of Sn
Reference 5
Source-reported events for the cited work
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Observation 5ff2176f-1656-4674-b518-57d680af6a49 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Microstructure characterization and elastic-plastic self- consistent simulation studies of anisotropic deformation of β-tin
Reference 6
Source-reported events for the cited work
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Observation 7268a35b-2a25-4ae5-b567-f960cc73fc3f · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Calibration and validation of the foundation for a multiphase strength model for tin
Reference 7
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Observation c278f3ba-a9a6-460a-b4a5-a5872da36aa3 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials A surprising proliferation of detwinning inβ-tin at extreme loading rates
Reference 8
Source-reported events for the cited work
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Observation 86fd41ea-7097-4bad-a6dc-469e27a06dc2 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Microscale deformation behavior of bicrystal boundaries in pure tin (Sn) using micropillar compression
Reference 9
Source-reported events for the cited work
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Observation 326ce497-b22f-4eb6-b179-be52e565b317 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Characterization of tri-lab β-tin (Sn)
Reference 10
Source-reported events for the cited work
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Observation b0964b58-5cf3-41b6-8c9a-d3a05d9f89a0 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Measurements of the grain boundary energy and anisotropy in tin
Reference 11
Source-reported events for the cited work
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Observation d78b1ab2-f2a5-4d51-8403-556114d1cfe3 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials The orientation imaging microscopy of lead-free Sn-Ag solder joints
Reference 12
Source-reported events for the cited work
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Observation c58a3492-fae4-43f4-8312-6c8fc4aa4c12 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Beta-tin grain formation in aluminum-modified lead-free solder alloys
Reference 13
Source-reported events for the cited work
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Observation 521322c1-3c3b-4f96-8c80-afa1664e2830 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Electrodeposition current density induced texture and grain boundary engineering in Sn coatings for enhanced corrosion resistance
Reference 14
Source-reported events for the cited work
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Observation 3ad17098-13db-4b9c-bc67-627f95d5e1ca · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Hybrid additive manufacturing of island grain bicrystals
Reference 15
Source-reported events for the cited work
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Observation 56beb255-4d0b-4725-ac7b-f3f48a21f4e4 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Spontaneous whisker growth on lead-free solder finishes
Reference 16
Source-reported events for the cited work
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Observation 5022e114-bfe7-4930-ae22-ea821ce403b6 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials A model of Sn whisker growth by coupled plastic flow and grain boundary diffusion
Reference 17
Source-reported events for the cited work
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Observation 43533731-4d2d-4f22-808d-c4042ba4386a · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Whisker growth in Sn coatings: A review of current status and future prospects
Reference 18
Source-reported events for the cited work
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Observation b10f5cc5-956b-4d70-abe3-05304dbd7f3a · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Growth twins and deformation twins in metals
Reference 19
Source-reported events for the cited work
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Observation bc838157-1902-448f-801d-d754e5e9ec10 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Disconnections and other defects associated with twin interfaces
Reference 20
Source-reported events for the cited work
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Observation d9e11b95-89f8-44b0-aaff-a50bf8882f14 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Twin-solute, twin-dislocation and twin-twin interactions in magne- sium
Reference 21
Source-reported events for the cited work
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Observation df9d0106-b5b8-4dec-a749-caf7bc4448a1 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Three-dimensional character of the deformation twin in magnesium
Reference 22
Source-reported events for the cited work
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Observation 1917ba58-c142-4d97-ae12-2e9f6eadd6cc · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials The effects of stress, temperature and facet struc- ture on growth of{10¯12} twins in Mg: A molecular dynamics and phase field study
Reference 23
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation bb0478f7-5b7a-4562-9678-924b6c9e8263 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Atomistic-informed phase field modeling of magnesium twin growth by disconnections
Reference 24
Source-reported events for the cited work
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Observation 4a82e068-9ca3-4a61-93f1-7086b9187207 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Formation and stability of long basal-prismatic facets in Mg
Reference 25
Source-reported events for the cited work
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Observation 136b3485-650a-4dfd-a774-8a51c1509ea1 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Semiempirical, quantum mechanical calculation of hydrogen embrittle- ment in metals
Reference 26
Source-reported events for the cited work
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Observation 8ab8d2dd-ee2b-4ce4-b0e9-ae1454371985 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Embedded-atom method: Derivation and application to impurities, surfaces, and other defects in metals
Reference 27
Source-reported events for the cited work
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Observation 8a3b5782-044c-41cb-9505-30701d7a6cf1 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Modified embedded-atom potentials for cubic materials and impurities
Reference 28
Source-reported events for the cited work
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Observation 8436daf2-f329-4cb0-8171-1b32ba562925 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Structural stability and lattice defects in copper: Ab initio, tight-binding, and embedded-atom calculations
Reference 29
Source-reported events for the cited work
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Observation 74e66ff5-a253-4c65-9ded-107c2d172a03 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials EAM potential for magnesium from quantum mechanical forces
Reference 30
Source-reported events for the cited work
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Observation f5a72338-015f-4258-9836-ed42437716e6 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Detwinning mechanisms for growth twins in face-centered cubic metals
Reference 31
Source-reported events for the cited work
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Observation 2e1caa39-7769-45b1-950e-771b6a59cc0e · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Atomic structures of symmetric tilt grain boundaries in hexagonal close packed (hcp) crystals
Reference 32
Source-reported events for the cited work
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Observation 796a7303-e70c-452d-bac0-90aaae4815e9 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Atomistic simulations of pure tin based on a new modified embedded-atom method interatomic potential
Reference 33
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Observation 43166e00-2e57-49b1-b4d3-ff8fe066f826 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Performance and cost assessment of machine learning interatomic potentials
Reference 34
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Observation 77154446-d558-4695-be42-f1a7f2951d90 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Machine-learning interatomic potentials for materials science
Reference 35
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Observation 443660d1-3c7f-401d-a8be-63ce41ea8336 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Hybrid interatomic potential for Sn
Reference 36
Source-reported events for the cited work
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Observation 92ae2c26-9505-4bce-8f35-a4bad57fe7b1 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials LAMMPS — a flexible simulation tool for particle- based materials modeling at the atomic, meso, and continuum scales
Reference 37
Source-reported events for the cited work
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Observation 7e6403a4-0f3c-4b77-8545-7f7776970f2f · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Ab-initio simulations of materials using VASP: Density-functional theory and beyond
Reference 38
Source-reported events for the cited work
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Observation 16510a30-4b5b-4544-815b-fd74ba77cdaf · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Generalized gradient approximation made simple
Reference 39
Source-reported events for the cited work
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Observation 69b78c0d-355b-4440-ac14-8197625e5520 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Visualization and analysis of atomistic simulation data with OVITO – the open visual- ization tool
Reference 40
Source-reported events for the cited work
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Observation 677bffe8-7a6f-46c8-8bf5-0f8b4958ef59 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Robust structural identification via polyhedral template matching
Reference 41
Source-reported events for the cited work
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Observation 5ce38166-0416-4e06-a647-2730f71e7b16 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Moment Tensor Potentials: a class of systematically improvable interatomic potentials
Reference 42
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Observation e6f958ff-bbde-4c9b-aba6-13bd6bd7e42c · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials The MLIP package: moment tensor potentials with MPI and active learning
Reference 43
Source-reported events for the cited work
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Observation 31633ab7-2b9b-43ee-9ee9-6c13e4a4ef2e · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials LAMMPS implementation of rapid artificial neural network derived interatomic potentials
Reference 44
Source-reported events for the cited work
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Observation 67543f69-eb77-4878-807e-2dc5377ca54b · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Grand canonically optimized grain boundary phases in hexagonal close-packed titanium
Reference 45
Source-reported events for the cited work
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Observation 0d93c5f5-c15a-44ed-b924-c6b1e040f35d · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Survey of computed grain boundary properties in face-centered cubic metals: I. Grain boundary energy
Reference 46
Source-reported events for the cited work
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Observation 18b677f1-f503-4fd5-99bd-3f0a52fd1594 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Extra variable in grain boundary description
Reference 47
Source-reported events for the cited work
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Observation 5febd32f-70b2-4e60-b85d-af21cd055ac9 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Predicting phase behavior of grain boundaries with evolutionary search and machine learning
Reference 48
Source-reported events for the cited work
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Observation 2ee8d380-9e56-4fba-83ad-cf4577370df1 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials The atomic simulation environment—a Python library for working with atoms
Reference 49
Source-reported events for the cited work
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Observation 9009197c-669a-4cbb-a36f-3b0776daf93e · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Role of the mesoscale in migration kinetics of flat grain boundaries
Reference 50
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation ce8bcbbc-9ebb-4c80-babc-53a066b41a9c · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials An interferometric study of grain boundary grooves in tin
Reference 51
Source-reported events for the cited work
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Observation 2d920fca-1553-4fdf-811e-10804356e31b · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Effect of twin grain boundary on the diffusion of Cu in bulk β-Sn
Reference 52
Source-reported events for the cited work
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Observation 5aa6002a-3ac1-411c-b3d7-6249ee5f40dc · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Free energy of grain boundary phases: Atomistic calculations for Σ5 (310) [001] grain boundary in Cu
Reference 53
Source-reported events for the cited work
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Observation 63f0c4a4-5063-4925-bb62-e78707d3490f · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Grain boundary mediated plasticity: On the evaluation of grain boundary migration-shear coupling
Reference 54
Source-reported events for the cited work
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Observation 1143ebd2-038f-4caa-8876-8484413e14d4 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Grain-boundary kinetics: A unified approach
Reference 55
Source-reported events for the cited work
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Observation cf355d7a-e6f2-4366-b4ac-8c187bb8404e · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Optimal transportation of grain boundaries: A forward model for predicting migration mechanisms
Reference 56
Source-reported events for the cited work
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Observation 151736e3-f2de-400d-a57f-64ab31aa85a6 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Grain boundary migration in polycrystals
Reference 57
Source-reported events for the cited work
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Observation 42b2d49c-1522-401d-b74e-7010a4af8c64 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials An atomistic survey of shear coupling in asymmetric 32 tilt grain boundaries and interpretation using the disconnections framework
Reference 58
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 6217603e-c837-409b-a012-069c367955de · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Coupling grain boundary motion to shear deformation
Reference 59
Source-reported events for the cited work
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Observation 31710af2-d0af-42f7-86b6-c94989655363 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Shear-driven motion of Mg{10¯12} twin boundaries via disconnection terrace nucleation, growth, and coalescence
Reference 60
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 6f415cfc-5796-48d5-9c36-5058d8384383 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Determination of all misorientations of tetragonal lattices with low multiplicity; connec- tion with Mallard’s rule of twinning
Reference 61
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 96bef9bf-39d9-4525-8f33-3a47dc51c84e · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Twinning by reticular pseudo-merohedry in trigonal, tetragonal and hexagonal crystals
Reference 62
Source-reported events for the cited work
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Observation d9baf8be-a090-4562-a8de-f134f00742ed · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Interface dislocations and grain boundary discon- nections using smith normal bicrystallography
Reference 63
Source-reported events for the cited work
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Observation fa663d2d-05f7-488a-88eb-f9de29fff6a9 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials On the importance of prismatic/basal interfaces in the growth of (-1012) twins in hexagonal close-packed crystals
Reference 64
Source-reported events for the cited work
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Observation 8fee9f02-f525-4e93-9419-b677ffa1dbbf · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Interface structures and twinning mechanisms of twins in hexagonal metals
Reference 65
Source-reported events for the cited work
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Observation 5cfd1800-a679-4cdc-b2b2-3cbd4dcb26fe · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials An analytical model of interfacial energy based on a lattice-matching interatomic energy
Reference 66
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 4f32df52-2b3e-435c-ab3f-ad294dea265e · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials A relaxation method for the energy and morphology of grain boundaries and interfaces
Reference 67
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation d320ed03-b63d-497d-8ef5-f79c7f282818 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials The growth of twins in tin single crystals as observed by transmission electron microscopy
Reference 68
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 5085a320-a98c-4578-89a6-df40d419c8f5 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Why are{10¯12} twins profuse in magnesium?
Reference 69
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 507ea197-cdff-416c-9d25-88b0e5fc679b · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Machine learning models for predictive materials science from fundamental physics: An application to titanium and zirconium
Reference 70
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 5bd38c65-8433-40ee-8bd8-cd45993eb2c7 · outbound
The structure and migration of twin boundaries in tetragonal $\beta$-Sn: an application of machine learning based interatomic potentials Unresolved cited work
Reference 71
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
No inbound Pith citation observations are available.