CHILI: Ch emically- I nformed L arge-scale I norganic Nanomaterials Dataset for Advancing Graph Machine Learning-Reference-Cited by-同舟云学术

CHILI: Ch emically- I nformed L arge-scale I norganic Nanomaterials Dataset for Advancing Graph Machine Learning

Published:2024-08-24 Issue: Volume:88 Page:4962-4973
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Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Friis-Jensen Ulrik¹^ORCID,Johansen Frederik L.²^ORCID,Anker Andy S.³^ORCID,Dam Erik B.⁴^ORCID,Jensen Kirsten M. Ø.⁵^ORCID,Selvan Raghavendra⁴^ORCID

Affiliation:

1. Department of Chemistry, University of Copenhagen & Department of Computer Science, University of Copenhagen, Copenhagen, Denmark

2. Department of Computer Science, University of Copenhagen & Department of Chemistry, University of Copenhagen, Copenhagen, Denmark

3. Dept. of Energy Conversion & Storage, Technical University of Denmark & Department of Chemistry, University of Oxford, Lyngby, Denmark

4. Department of Computer Science, University of Copenhagen, Copenhagen, Denmark

5. Department of Chemistry, University of Copenhagen, Copenhagen, Denmark

Funder

European Research Council

Data Plus Grant, UCPH

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671538

Reference94 articles.

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4. Ilyes Batatia, David P Kovacs, Gregor Simm, Christoph Ortner, and Gábor Csányi. 2022. MACE: Higher order equivariant message passing neural networks for fast and accurate force fields. Advances in Neural Information Processing Systems (NeurIPS) (2022).