Evidence map›Paper›PMID 36074116›Full record

ArticleJournal of chemical information and modeling2022

QCforever: A Quantum Chemistry Wrapper for Everyone to Use in Black-Box Optimization.

Masato Sumita, Kei Terayama, Ryo Tamura, Koji Tsuda

Abstract read
In one paragraph

Article in Journal of chemical information and modeling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

4 authors.

Masato SumitaRIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan.ORCID 0000-0002-3506-1028
Kei TerayamaRIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan.ORCID 0000-0003-3914-248X
Ryo TamuraRIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan.ORCID 0000-0002-0349-358X
Koji TsudaRIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan.ORCID 0000-0002-4288-1606

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To obtain observable physical or molecular properties such as ionization potential and fluorescent wavelength with quantum chemical (QC) computation, multi-step computation manipulated by a human is required. Hence, automating the multi-step computational process and making it a black box that can be handled by anybody are important for effective database construction and fast realistic material design through the framework of black-box optimization where machine learning algorithms are introduced as a predictor. Here, we propose a Python library, QCforever, to automate the computation of some molecular properties and chemical phenomena induced by molecules. This tool just requires a molecule file for providing its observable properties, automating the computation process of molecular properties (for ionization potential, fluorescence, etc.) and output analysis for providing their multi-values for evaluating a molecule. Incorporating the tool in black-box optimization, we can explore molecules that have properties we desired within the limitation of QC computation.

Indexed as

AlgorithmsMachine LearningDatabases, FactualHumans

Identifiers

PMID36074116
PMCPMC9518232

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.