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uq-polymer-tg's Introduction

Uncertainty Quantification in Machine Learning for Glass Transition Temperature Prediction of Polymers

Code repository for the above titled paper

Workflow

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Dataset

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  • Dataset 1: 6097 homopolymers with Tg from PoLyInfo
  • Dataset 2: 240 homopolymers with Tg from experiment data
  • Dataset 3: 566 homopolymers with Tg from MD simulation
  • High-Tg polymers (Tg>350℃): 19 high-Tg polymers from experiment data

Methods

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  • Neural network ensemble: Pytorch
  • Gaussian process regression (GPR): GPy
  • Monte Carlo dropout (MCD): Pytorch
  • Mean-variance estimation (MVE): Pytorch
  • Bayesian neural network (BNN): Pytorch
  • Evidential deep learning (EDL): Pytorch, Chemprop

Input

  • Morgan fingerprint with frequency: Considering the number of substructures

Output

  • Mean and standard deviations of Tg for homopolyers

Metrics

  • Spearman's rank correlation coefficient
  • Calibration
  • Sparsification

uq-polymer-tg's People

Contributors

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Watchers

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Forkers

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