materialscloud:2020.57

Polymer descriptor data set for machine learning prediction of specific heat

Rahul Bhowmik1*

1 Polaron Analytics, Suite 450, 4031 Colonel Glenn Highway, Beavercreek, OH 45431, USA

* Corresponding authors emails: bhowmikrahul@gmail.com
DOI10.24435/materialscloud:18-nr [version v1]

Publication date: Jun 19, 2020

How to cite this record

Rahul Bhowmik, Polymer descriptor data set for machine learning prediction of specific heat, Materials Cloud Archive 2020.57 (2020), doi: 10.24435/materialscloud:18-nr.

Description

We have developed a polymer descriptor data set from the existing data. The data set has 188 descriptors which describe polymer atomic and molecular behavior. The descriptors are mapped to the specific heat of polymers using supervised and unsupervised machine learning approaches. The mapping helps predict the specific heat of polymers at room temperature. The descriptor data set is useful in synthesizing novel polymers with desired heat capacities.

Materials Cloud sections using this data

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Files

File name Size Description
Cp_Polymer_Data.csv
MD5md5:911b6afd3434ad16a3ed4f114dda29f1
29.6 KiB Polymer descriptor data set is used to map the 188 descriptors to heat capacity using machine learning method, and useful to predict the heat capacities of polymers
README.txt
MD5md5:58e2e6f030d1501a3d03965a46f32694
546 Bytes The file listed the atom names used in various columns of the file, "Cp_Polymer_Data.csv."
structures.zip
MD5md5:988baafdc6c5f7b9c51c61dc433010b1
104.6 KiB The folder contains all the structure files of polymers used to generate the descriptors.

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.

External references

Journal reference (The citation will be updated once accepted for publication.)
R. Bhowmik, S. Sihn, R. Pachter, J. P. Vernon, (submitted to ACS Applied Polymer Materials)

Keywords

polymer specific heat descriptors

Version history:

2020.57 (version v1) [This version] Jun 19, 2020 DOI10.24435/materialscloud:18-nr