Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation

Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation

Machine learning and artificial intelligence (AI/ML) methods are beginning to have significant impact in chemistry and condensed matter physics. For example, deep learning methods have demonstrated new capabilities for high-throughput virtual screening, and global optimization approaches for inverse...

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Bibliographic Details
Main Authors: Addis S. Fuhr, Bobby G. Sumpter
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-03-01
Series:Frontiers in Materials
Subjects:
machine learning
materials discovery
inverse design
generative adversarial networks
variational autoencoders
generative models
Online Access:https://www.frontiersin.org/articles/10.3389/fmats.2022.865270/full

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https://www.frontiersin.org/articles/10.3389/fmats.2022.865270/full

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