Data-Driven Strategies for Accelerated Materials Design-Reference-Cited by-同舟云学术

Data-Driven Strategies for Accelerated Materials Design

Published:2021-02-02 Issue:4 Volume:54 Page:849-860
ISSN:0001-4842
Container-title:Accounts of Chemical Research
language:en
Short-container-title:Acc. Chem. Res.

Author:

Pollice Robert¹²^ORCID,dos Passos Gomes Gabriel¹²^ORCID,Aldeghi Matteo¹²³^ORCID,Hickman Riley J.¹²,Krenn Mario¹²³,Lavigne Cyrille¹²^ORCID,Lindner-D’Addario Michael¹²,Nigam AkshatKumar¹²^ORCID,Ser Cher Tian¹²,Yao Zhenpeng¹²^ORCID,Aspuru-Guzik Alán¹²³⁴^ORCID

Affiliation:

1. Chemical Physics Theory Group, Department of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada

2. Department of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada

3. Vector Institute for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada

4. Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR), 661 University Ave, Toronto, Ontario M5G, Canada

Funder

U.S. Department of Defense

Schweizerischer Nationalfonds zur F??rderung der Wissenschaftlichen Forschung

Austrian Science Fund

Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada

Fonds de Recherche Quebec Nature et Technologies

US Department of Energy, Office of Science, Office of Basic Energy Sciences

US Department of Energy, Office of Science - Chicago

Queen Elizabeth II Graduate Scholarship in Science and Technology

Publisher

American Chemical Society (ACS)

Subject

General Medicine,General Chemistry

Link

http://pubs.acs.org/doi/pdf/10.1021/acs.accounts.0c00785

Reference74 articles.

1. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach

2. Inverse design of nanoporous crystalline reticular materials with deep generative models

3. Nigam, A.; Friederich, P.; Krenn, M.; Aspuru-Guzik, A. Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space. In International Conference on Learning Representations; 2020.

4. Phoenics: A Bayesian Optimizer for Chemistry

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