Assessing Deep Generative Models in Chemical Composition Space-Reference-Cited by-同舟云学术

Assessing Deep Generative Models in Chemical Composition Space

Published:2022-10-19 Issue:21 Volume:34 Page:9455-9467
ISSN:0897-4756
Container-title:Chemistry of Materials
language:en
Short-container-title:Chem. Mater.

Author:

Türk Hanna¹²^ORCID,Landini Elisabetta¹²,Kunkel Christian¹,Margraf Johannes T.¹^ORCID,Reuter Karsten¹

Affiliation:

1. Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195Berlin, Germany

2. Chair for Theoretical Chemistry and Catalysis Research Center, Department of Chemistry, Technische Universität München, Lichtenbergstraße 4, 85748Garching, Germany

Funder

Deutsche Forschungsgemeinschaft

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.chemmater.2c01860

Reference90 articles.

1. Brock, A.; Donahue, J.; Simonyan, K.Large Scale GAN Training for High Fidelity Natural Image Synthesis. 2019. arXiv preprint arXiv:1809.11096

2. Clark, A.; Donahue, J.; Simonyan, K.Adversarial Video Generation on Complex Datasets. 2019. arXiv preprint arXiv:1907.06571

3. Nie, W.; Narodytska, N.; Patel, A.RelGAN: Relational Generative Adversarial Networks for Text Generation. International Conference on Learning Representations, 2019.

4. Dong, H.W.; Hsiao, W.Y.; Yang, L.C.; Yang, Y.H.MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment. Proceedings of the Annual AAAI Conference on Artificial Intelligence, 2018.

5. De Novo Design of Bioactive Small Molecules by Artificial Intelligence

Cited by 13 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Has generative artificial intelligence solved inverse materials design?;Matter;2024-07

2. WyCryst: Wyckoff inorganic crystal generator framework;Matter;2024-06

3. Methods and applications of machine learning in computational design of optoelectronic semiconductors;Science China Materials;2024-03-19

4. Artificial intelligence generates novel 3D printing formulations;Applied Materials Today;2024-02

5. Generative adversarial networks and diffusion models in material discovery;Digital Discovery;2024