State-space reduction and equivalence class sampling for a molecular self-assembly model.

Daniel M Packwood, Patrick Han, Taro Hitosugi

Abstract read

In one paragraph

Article in Royal Society open science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it

0cells of the map it votes in

2citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

Materials informatics for self-assembly of functionalized organic precursors on metal surfaces.Nature communications · 2018
Article
Chemical and entropic control on the molecular self-assembly process.Nature communications · 2017
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Daniel M PackwoodAdvanced Institute for Materials Research (AIMR), Tohoku University, Sendai 980-8577, Japan; Japan Science and Technology Agency (PRESTO), Kawaguchi, Saitama 332-0012, Japan.ORCID 0000-0001-9387-728X

Patrick HanAdvanced Institute for Materials Research (AIMR), Tohoku University, Sendai 980-8577, Japan; California Nanosystems Institute, University of California, Los Angeles, CA 90095, USA; Department of Chemistry and Biochemistry, University of California, Los Angeles, CA 90095, USA; Department of Materials Science and Engineering, University of California, Los Angeles, CA 90095, USA.

Taro HitosugiAdvanced Institute for Materials Research (AIMR), Tohoku University, Sendai 980-8577, Japan; Department of Applied Chemistry, Graduate School of Science and Engineering, Tokyo Institute of Technology, Tokyo 152-8352, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Direct simulation of a model with a large state space will generate enormous volumes of data, much of which is not relevant to the questions under study. In this paper, we consider a molecular self-assembly model as a typical example of a large state-space model, and present a method for selectively retrieving 'target information' from this model. This method partitions the state space into equivalence classes, as identified by an appropriate equivalence relation. The set of equivalence classes H, which serves as a reduced state space, contains none of the superfluous information of the original model. After construction and characterization of a Markov chain with state space H, the target information is efficiently retrieved via Markov chain Monte Carlo sampling. This approach represents a new breed of simulation techniques which are highly optimized for studying molecular self-assembly and, moreover, serves as a valuable guideline for analysis of other large state-space models.

Indexed as

Markov chain Monte Carlomodel reductionself-assembly

Identifiers

PMID27493765

DOI10.1098/rsos.150681

PMCPMC4968457

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.