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Record W4408216598 · doi:10.1021/accountsmr.4c00296

Dislocation Loop Transformation in Metals: Computational Studies, Theoretical Prediction and Future Perspectives

2025· article· en· W4408216598 on OpenAlexafffund
Cheng Chen, Yiding Wang, Jie Hou, Jun Song

Bibliographic record

VenueAccounts of Materials Research · 2025
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaMcGill University
KeywordsTransformation (genetics)DislocationLoop (graph theory)Materials scienceStatistical physicsComputer sciencePhysicsMathematicsCondensed matter physicsChemistryCombinatorics

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Conspectus Dislocation loops (DLs), characterized by closed dislocation lines, are a category of defects of vital importance in determining the mechanical properties of metals, particularly under extreme conditions, such as irradiation, severe plastic deformation, and hydrogen embrittlement. These loops, more intricate than simple dislocations, exhibit far more intricate reaction and evolution pathways arising from the loop type transformation and the associated planar fault transition. This can significantly alter dislocation activities contributing to dislocation channels and complex dislocation networks, which are closely linked to crack initiation and propagation during fracture. Understanding the transformation of DLs is crucial for the development of materials capable of withstanding harsh environments, including those encountered in nuclear reactors, aerospace applications, and hydrogen-rich environments. This Account delves into the computational advancements in studying DL transformations in FCC, HCP, and BCC metals. Traditional simulations often struggle to capture the complexity of DL structures and interactions. To overcome these limitations, a novel computational approach has been developed, enabling precise construction and analysis of DLs. Not only does it automatically account for necessary atom addition or deletion, it is also generic and versatile, applicable for any arbitrary DL morphology with planar fault or fault combination in both pristine metal and complex alloy systems. The new construction approach of DLs provides a critical enabler for studying the transformation of DLs across different crystal structures. In high-symmetry FCC metals, these transformations involve complex unfaulting driven by Shockley and Frank loop interactions, influenced by variations in stress, temperature, and radiation. Meanwhile, HCP metals, with a lower crystal symmetry, exhibit more complex DL transformations due to high anisotropy in the slip systems, variation in Burgers vectors, and different planar faults. Unlike pristine FCC and HCP lattices, ordered intermetallic systems like L1 2 -Ni 3 Al experience a disruption of translational symmetry within the lattice. The ordered nature of these alloys complicates DL interacting with line dislocation, causing asymmetrical shearing and looping mechanisms. BCC metals, in contrast, exhibit different DL evolution due to the lack of stable stacking faults, leading to stronger interactions with impurities such as carbon and hydrogen. In particular, the interaction between DLs and hydrogen in BCC metals is a critical aspect worth investigating as it can cause severe damage in BCC materials under irradiation, hydrogen embrittlement, and intense deformation. This Account highlights the complex nature of DL transformation in metals under extreme environments and recent computational advances. Differences in the evolution of DLs across crystal structures and their interactions with cracks and solute elements are critical areas for future research. Key challenges include extending DL transformation theories to ordered lattice structures, developing machine-learning-based interatomic potentials, and refining multiscale models to better capture the dynamic behavior of DLs. These efforts will help develop more accurate predictive models, leading to materials with improved resistance to deformation and fracture in harsh environments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.393
Teacher spread0.353 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes2
Has abstractyes

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