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Record W4415877638 · doi:10.1021/acs.jpcc.5c05611

The Stability and Electronic Structure of Anti-MXenes

2025· article· en· W4415877638 on OpenAlexaff
Linh La, Salvy P. Russo, Truyen Tran, Svetha Venkatesh, Sherif Abdulkader Tawfik

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2025
Typearticle
Languageen
FieldMaterials Science
TopicMXene and MAX Phase Materials
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsElectronic structureSupercellDensity functional theoryStructural stabilityStability (learning theory)Range (aeronautics)Class (philosophy)

Abstract

fetched live from OpenAlex

Anti-MXenes are a new class of 2D materials with the formula M n X n +1, and they have recently been attracting attention. The successful synthesis of Mo 5 N 6 and the identification of its atomic arrangements (labeled as α and γ), along with the resolved structure of TiC 2, raise an important question: What are the possible atomic structures of M n X n +1 layers across different transition metals M and values of n? In this work, we identify a new structural prototype, denoted as β, through a combination of density functional theory (DFT) calculations and evolutionary optimization. By systematically exploring the stable polymorphs of 32 anti-MXenes structures with the formula M n X n +1 (M = Ti, V, Cr, Mn, Nb, Mo, Ta, and W; n = 2, 3, 4, 5), we find that the β structure is more favorable in thinner compositions ( n = 2) and tends to appear at the outermost layers of thicker structures ( n = 5), leading to hybrid structures that are mixtures of α/γ and β configurations. We identify a range of nontrivial electronic states in these materials: semimetals, flat bands, a semiconductor, and materials with high electronic density at the Fermi level. Further analysis of M 2 X 3 β structures using supercell models reveals that Mo 2 X 3 relaxes into a different lower-energy configuration. Our findings unveil the rich and complex structural and electronic landscape of anti-MXenes, which warrants further exploration of this class of materials.

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)0.000
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 score0.011
Threshold uncertainty score0.141

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.252
Teacher spread0.246 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry CSame topicMXene and MAX Phase MaterialsFrench-language works237,207