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Record W4396818077 · doi:10.1107/s2053273323087338

Experimentally validated <i>ab initio</i> crystal structure prediction of novel metal–organic framework materials

2023· article· en· W4396818077 on OpenAlexaff
Yizhi Xu, Joseph M. Marrett, Hatem M. Titi, James P. Darby, Andrew J. Morris, Tomislav Friščić, Mihails Arhangelskis

Bibliographic record

VenueActa Crystallographica Section A Foundations and Advances · 2023
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsMcGill University
FundersEngineering and Physical Sciences Research Council
KeywordsAb initioCrystal structure predictionMetal-organic frameworkMaterials scienceCrystal structureMetalComputational chemistryChemical physicsCrystallographyChemistryPhysical chemistryOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

Metal-organic frameworks (MOFs) are crystalline materials with a broad range of applications, including gas storage and separation, catalysis, drug delivery and energetic materials [1].The diversity of MOF applications is closely related to their modular node-and-linker composition of which various desired MOF properties can be designed.Currently, MOF design is principally based on empirical experimental approaches where geometry and chemical intuition are the main considerations when choosing possible nodes and linkers.In contrast, the development of ab initio computational methods for predicting structures and properties of MOFs could provide new, unexpected opportunities for a more general design and synthesis of such microporous materials.Recently, we have reported [2] the first-principles crystal structure prediction (CSP) method for MOFs, based on the ab initio random structure searching (AIRSS) [3] and Wyckoff Alignment of Molecules (WAM) algorithms, where several classes of existing MOF structures have been successfully reproduced.Herein, I will present the first examples for predicting three new rare zeolitic imidazolate frameworks (ZIFs) based on Cu 2+ ion nodes with imidazolate linkers containing unsaturated side substituents.Our predicted ZIF structures matched the subsequently synthesized materials, presenting the first example of MOFs being designed with the aid of CSP.Moreover, our specific focus on the unsaturated substituted imidazolate linkers with vinyl and acetylene moieties yield ZIFs that exhibit hypergolic behaviour [4,5].The experimentally-observed ZIFs revealed excellent hypergolic ignition properties, and more importantly, the high energy densities were derived from CSP calculations, showing our ability to predict not only structures but also properties of the ab initio designed MOFs.Overall, this study demonstrates how our ab initio approach to MOF design can lead to major advances in MOF development, while also improving our understanding of MOF polymorphism and structure-property relationships.Figure 1.(A) Chemical diagram of copper(II)-based ZIFs.(B) The three 2-substituted imidazole linkers used in this work.(C) Calculated CSP energy landscape of Cu(AIm)2.Each dot is representing a unique crystal structure coloured by its Cu coordination geometry index (τ4).The structure of the global energy minimum α-Cu(AIm)2 is shown while the red dot is representing the β-Cu(AIm)2 structure generated via perturbation analysis.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.235
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.265
Teacher spread0.247 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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