3DReact: Geometric Deep Learning for Chemical Reactions-Reference-Cited by-同舟云学术

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3DReact: Geometric Deep Learning for Chemical Reactions

Published:2024-07-15 Issue:15 Volume:64 Page:5771-5785
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

van Gerwen Puck¹²^ORCID,Briling Ksenia R.¹^ORCID,Bunne Charlotte²³^ORCID,Somnath Vignesh Ram²³^ORCID,Laplaza Ruben¹²^ORCID,Krause Andreas²³,Corminboeuf Clemence¹²^ORCID

Affiliation:

1. Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

2. National Center for Competence in Research − Catalysis (NCCR-Catalysis), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

3. Learning & Adaptive Systems Group, Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland

Funder

NCCR Catalysis

National Center of Competence in Research Materials? Revolution: Computational Design and Discovery of Novel Materials

H2020 European Research Council

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.4c00104

Reference143 articles.

1. Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces

2. Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

3. On representing chemical environments

4. Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

5. Huo, H.; Rupp, M. Unified representation for machine learning of molecules and crystals. arXiv preprint 2017, arXiv:1704.06439.

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