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Record W2985508180 · doi:10.1103/physreve.100.052404

Phase behavior of blocky charge lattice polymers: Crystals, liquids, sheets, filaments, and clusters

2019· article· en· W2985508180 on OpenAlexafffund
Nicholas A. S. Robichaud, Ivan Saika‐Voivod, Stefan Wallin

Bibliographic record

VenuePhysical review. E · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhase diagramChemical physicsCharge densityLattice (music)PolymerMaterials scienceMonte Carlo methodCluster (spacecraft)MonomerCharge (physics)Valence (chemistry)CrystallographyPhase (matter)Molecular physicsPhysicsChemistry

Abstract

fetched live from OpenAlex

Motivated by the idea that intrinsically disordered proteins (IDPs) condense into liquidlike droplets within cells, we carry out Monte Carlo simulations of a polymer lattice model to study the relationship between charge patterning and phase separation. Polymer chains containing neutral, positively charged, and negatively charged monomers are placed on a cubic lattice. Only nearest-neighbor interactions between charges are considered. We determine the phase diagram for a systematically varied set of sequences. We observe homogeneous fluids, liquid condensation, cluster phases, filaments, and crystal states. Of the six sequences we study, three form crystals at low temperatures. The other three sequences, which have lower charge densities, instead collapse into gel-like networks or unconnected finite clusters. Longer neutral patches along the sequence sterically limit the size and shape of low-energy structures, which is analogous to the effect of charge or limited valence in attractive colloids. Only one sequence clearly exhibits liquid behavior; this sequence has a reduced tendency to individually fold and crystallize compared to others of similar charge density and draws parallels to real IDP behavior.

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 score0.062
Threshold uncertainty score0.438

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 head0.014
GPT teacher head0.344
Teacher spread0.330 · 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

Citations14
Published2019
Admission routes2
Has abstractyes

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