Generative adversarial networks and diffusion models in material discovery-Reference-Cited by-同舟云学术

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Generative adversarial networks and diffusion models in material discovery

Published:2024 Issue:1 Volume:3 Page:62-80
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Alverson Michael¹²^ORCID,Baird Sterling G.¹,Murdock Ryan¹,Ho (Enoch) Sin-Hang³,Johnson Jeremy³^ORCID,Sparks Taylor D.¹^ORCID

Affiliation:

1. Department of Materials Science & Engineering, University of Utah, Salt Lake City, 84112, Utah, USA

2. Thomas Lord Department of Computer Science, University of Southern California, Los Angeles, 90089, California, USA

3. Department of Chemistry & Biochemistry, Brigham Young University, Provo, 84602, Utah, USA

Abstract

Diffusion Models outperform Generative Adversarial Networks (GANs) and Wasserstein GANs in material discovery.

Funder

National Science Foundation

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/DD/D3DD00137G

Reference69 articles.

1. Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods

2. Combinatorial Synthesis of Magnesium Tin Nitride Semiconductors

3. The art and practice of structure-based drug design: A molecular modeling perspective

4. Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials

5. Machine-learning-assisted search for functional materials over extended chemical space

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