Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning

Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning

Abstract In recent years, machine learning (ML) techniques are seen to be promising tools to discover and design novel materials. However, the lack of robust inverse design approaches to identify promising candidate materials without exploring the entire design space causes a fundamental bottleneck....

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Bibliographic Details
Main Authors: Chun‐Teh Chen, Grace X. Gu
Format: Article
Language:English
Published: Wiley 2020-03-01
Series:Advanced Science
Subjects:
composites
inverse problem
machine learning
materials design
optimization algorithms
Online Access:https://doi.org/10.1002/advs.201902607

Internet

https://doi.org/10.1002/advs.201902607

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