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Materials Genome Software Framework: Scalable Parallel Simulation, Virtual Reality Visualization and Machine Learning

  • July 2019
  • Conference: The 17th International Conference on Scientific Computing
  • At: Las Vegas, USA
Authors:
Ankit Mishra at University of Southern California
Ankit Mishra
Nitish Baradwaj
Nitish Baradwaj
Nitish Baradwaj
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Lindsay Bassman Oftelie at Lawrence Berkeley National Laboratory
Lindsay Bassman Oftelie
Brandon K. Horton
Brandon K. Horton
Brandon K. Horton
  • This person is not on ResearchGate, or hasn't claimed this research yet.
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Int'l Conf. Scientific Computing | CSC'19 |
125
ISBN: 1-60132-494-4, CSREA Press ©
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Int'l Conf. Scientific Computing | CSC'19 |
ISBN: 1-60132-494-4, CSREA Press ©
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Int'l Conf. Scientific Computing | CSC'19 |
127
ISBN: 1-60132-494-4, CSREA Press ©
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Int'l Conf. Scientific Computing | CSC'19 |
ISBN: 1-60132-494-4, CSREA Press ©
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Int'l Conf. Scientific Computing | CSC'19 |
129
ISBN: 1-60132-494-4, CSREA Press ©
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130
Int'l Conf. Scientific Computing | CSC'19 |
ISBN: 1-60132-494-4, CSREA Press ©
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
Int'l Conf. Scientific Computing | CSC'19 |
131
ISBN: 1-60132-494-4, CSREA Press ©
... 11,12 Compared to the vast success of machine learning (ML) for predicting structure−property relationships in molecular and material research, 7 predicting the dielectric properties of polymers (i.e., dielectric polymer genome) is still in its infancy. 13−15 Recently, computationally synthesized polymer databases have been created, 16 which involved highly accurate computation of dielectric constants based on the new generation of first-principles-informed polarizable reactive force field methods. 17−19 These new computational databases provide an ideal testing ground for the quantitative assessment of ML models for the dielectric polymer genome within a wellcontrollable environment. ...
... i opt (16) with respect to the initial values of weights and biases. The initial weighting parameters of the hidden layer ω jl (1) (1 ≤ j ≤ m, 1 ≤ l ≤ n) remain fixed when varying α and can be expressed as ...
Dielectric Polymer Property Prediction Using Recurrent Neural Networks with Optimizations
Article
  • Apr 2021
Despite the growing success of machine learning for predicting structure-property relationships in molecules and materials, such as predicting the dielectric properties of polymers, it is still in its infancy. We report on the effectiveness of solving structure-property relationships for a computer-generated database of dielectric polymers using recurrent neural network (RNN) models. The implementation of a series of optimization strategies was crucial to achieving high learning speeds and sufficient accuracy: (1) binary and nonbinary representations of SMILES (Simplified Molecular Input Line System) fingerprints and (2) backpropagation with affine transformation of the input sequence (ATransformedBP) and resilient backpropagation with initial weight update parameter optimizations (iRPROP- optimized). For the investigated database of polymers, the binary SMILES representation was found to be superior to the decimal representation with respect to the training and prediction performance. All developed and optimized Elman-type RNN algorithms outperformed nonoptimized RNN models in the efficient prediction of nonlinear structure-activity relationships. The average relative standard deviation (RSD) remained well below 5%, and the maximum RSD did not exceed 30%. Moreover, we provide a C++ codebase as a testbed for a new generation of open programming languages that target increasingly diverse computer architectures.
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