Polymers for Extreme Conditions Designed Using Syntax-Directed
Variational Autoencoders
- URL: http://arxiv.org/abs/2011.02551v1
- Date: Wed, 4 Nov 2020 21:36:59 GMT
- Title: Polymers for Extreme Conditions Designed Using Syntax-Directed
Variational Autoencoders
- Authors: Rohit Batra, Hanjun Dai, Tran Doan Huan, Lihua Chen, Chiho Kim, Will
R. Gutekunst, Le Song, Rampi Ramprasad
- Abstract summary: Machine learning tools are now commonly employed to virtually screen material candidates with desired properties.
This approach is inefficient, and severely constrained by the candidates that human imagination can conceive.
We utilize syntax-directed variational autoencoders (VAE) in tandem with Gaussian process regression (GPR) models to discover polymers expected to be robust under three extreme conditions.
- Score: 53.34780987686359
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The design/discovery of new materials is highly non-trivial owing to the
near-infinite possibilities of material candidates, and multiple required
property/performance objectives. Thus, machine learning tools are now commonly
employed to virtually screen material candidates with desired properties by
learning a theoretical mapping from material-to-property space, referred to as
the \emph{forward} problem. However, this approach is inefficient, and severely
constrained by the candidates that human imagination can conceive. Thus, in
this work on polymers, we tackle the materials discovery challenge by solving
the \emph{inverse} problem: directly generating candidates that satisfy desired
property/performance objectives. We utilize syntax-directed variational
autoencoders (VAE) in tandem with Gaussian process regression (GPR) models to
discover polymers expected to be robust under three extreme conditions: (1)
high temperatures, (2) high electric field, and (3) high temperature \emph{and}
high electric field, useful for critical structural, electrical and energy
storage applications. This approach to learn from (and augment) human ingenuity
is general, and can be extended to discover polymers with other targeted
properties and performance measures.
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