Article ; Online: Large-Scale Glass-Transition Temperature Prediction with an Equivariant Neural Network for Screening Polymers.
ACS omega
2024 Volume 9, Issue 5, Page(s) 5452–5462
Abstract: The practically infinite chemical and morphological space of polymers makes them pervasive with applications in materials science but challenges the rational discovery of new materials with favorable properties. Polymer informatics aims to accelerate ... ...
Abstract | The practically infinite chemical and morphological space of polymers makes them pervasive with applications in materials science but challenges the rational discovery of new materials with favorable properties. Polymer informatics aims to accelerate materials design through property prediction and large-scale virtual screening. In this study, a new method (Lieconv-Tg) has been developed to predict glass-transition temperature (Tg) values from repeating units of polymers based on Lieconv, which is equivariant with transformations from any specified Lie group. The introduction of equivariance allows the prediction of molecular properties from their 3D structures, independent of orientation and position. A total of 27,659 homopolymers with Tg values were collected from PolyInfo, and a standard data set containing 7166 polymers (named data set_Tg) was created for training a robust Lieconv-Tg model. Using the 3D coordinates as input, Lieconv-Tg performs better than Edge-Conditioned Convolution (ECC), and the mean absolute error (MAE) is significantly reduced by ∼6 from ∼30 to ∼24 on both the validation set and the test set, and the |
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Language | English |
Publishing date | 2024-01-26 |
Publishing country | United States |
Document type | Journal Article |
ISSN | 2470-1343 |
ISSN (online) | 2470-1343 |
DOI | 10.1021/acsomega.3c06843 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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