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Domain-informed graph neural networks: A quantum chemistry case study

Jay Paul Morgan Orcid Logo, Adeline Paiement Orcid Logo, Christian Klinke

Neural Networks, Volume: 165, Pages: 938 - 952

Swansea University Authors: Jay Paul Morgan Orcid Logo, Adeline Paiement Orcid Logo, Christian Klinke

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DOI (Published version): 10.1016/j.neunet.2023.06.030

Abstract

We explore different strategies to integrate prior domain knowledge into the design of graph neural networks (GNN). Our study is supported by a use-case of estimating the potential energy of chemical systems (molecules and crystals) represented as graphs. We integrate two elements of domain knowledg...

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Published in: Neural Networks
ISSN: 0893-6080 1879-2782
Published: Elsevier BV 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa63771
first_indexed 2023-07-04T09:35:26Z
last_indexed 2024-11-25T14:12:54Z
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spelling 2024-09-19T11:15:34.4133821 v2 63771 2023-07-04 Domain-informed graph neural networks: A quantum chemistry case study df9a27bcf77b4769c2ebbb702b587491 0000-0003-3719-362X Jay Paul Morgan Jay Paul Morgan true false f50adf4186d930e3a2a0f9a6d643cf53 0000-0001-5114-1514 Adeline Paiement Adeline Paiement true false c10c44238eabfb203111f88a965f5372 Christian Klinke Christian Klinke true false 2023-07-04 MACS We explore different strategies to integrate prior domain knowledge into the design of graph neural networks (GNN). Our study is supported by a use-case of estimating the potential energy of chemical systems (molecules and crystals) represented as graphs. We integrate two elements of domain knowledge into the design of the GNN to constrain and regularise its learning, towards higher accuracy and generalisation. First, knowledge on the existence of different types of relations/graph edges (e.g. chemical bonds in our case study) between nodes of the graph is used to modulate their interactions. We formulate and compare two strategies, namely specialised message production and specialised update of internal states. Second, knowledge of the relevance of some physical quantities is used to constrain the learnt features towards a higher physical relevance using a simple multi-task learning (MTL) paradigm. We explore the potential of MTL to better capture the underlying mechanisms behind the studied phenomenon. We demonstrate the general applicability of our two knowledge integrations by applying them to three architectures that rely on different mechanisms to propagate information between nodes and to update node states. Our implementations are made publicly available. To support these experiments, we release three new datasets of out-of-equilibrium molecules and crystals of various complexities. Journal Article Neural Networks 165 938 952 Elsevier BV 0893-6080 1879-2782 Graph neural network, Domain knowledge integration, Quantum chemistry application 31 8 2023 2023-08-31 10.1016/j.neunet.2023.06.030 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University 2024-09-19T11:15:34.4133821 2023-07-04T10:30:46.7161905 Faculty of Science and Engineering School of Engineering and Applied Sciences - Chemistry Jay Paul Morgan 0000-0003-3719-362X 1 Adeline Paiement 0000-0001-5114-1514 2 Christian Klinke 3 63771__28032__1c504db564384288b477322ad88dbd58.pdf 63771.pdf 2023-07-04T10:34:58.9221881 Output 1480288 application/pdf Accepted Manuscript true 2024-07-01T00:00:00.0000000 false eng
title Domain-informed graph neural networks: A quantum chemistry case study
spellingShingle Domain-informed graph neural networks: A quantum chemistry case study
Jay Paul Morgan
Adeline Paiement
Christian Klinke
title_short Domain-informed graph neural networks: A quantum chemistry case study
title_full Domain-informed graph neural networks: A quantum chemistry case study
title_fullStr Domain-informed graph neural networks: A quantum chemistry case study
title_full_unstemmed Domain-informed graph neural networks: A quantum chemistry case study
title_sort Domain-informed graph neural networks: A quantum chemistry case study
author_id_str_mv df9a27bcf77b4769c2ebbb702b587491
f50adf4186d930e3a2a0f9a6d643cf53
c10c44238eabfb203111f88a965f5372
author_id_fullname_str_mv df9a27bcf77b4769c2ebbb702b587491_***_Jay Paul Morgan
f50adf4186d930e3a2a0f9a6d643cf53_***_Adeline Paiement
c10c44238eabfb203111f88a965f5372_***_Christian Klinke
author Jay Paul Morgan
Adeline Paiement
Christian Klinke
author2 Jay Paul Morgan
Adeline Paiement
Christian Klinke
format Journal article
container_title Neural Networks
container_volume 165
container_start_page 938
publishDate 2023
institution Swansea University
issn 0893-6080
1879-2782
doi_str_mv 10.1016/j.neunet.2023.06.030
publisher Elsevier BV
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Chemistry{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Chemistry
document_store_str 1
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description We explore different strategies to integrate prior domain knowledge into the design of graph neural networks (GNN). Our study is supported by a use-case of estimating the potential energy of chemical systems (molecules and crystals) represented as graphs. We integrate two elements of domain knowledge into the design of the GNN to constrain and regularise its learning, towards higher accuracy and generalisation. First, knowledge on the existence of different types of relations/graph edges (e.g. chemical bonds in our case study) between nodes of the graph is used to modulate their interactions. We formulate and compare two strategies, namely specialised message production and specialised update of internal states. Second, knowledge of the relevance of some physical quantities is used to constrain the learnt features towards a higher physical relevance using a simple multi-task learning (MTL) paradigm. We explore the potential of MTL to better capture the underlying mechanisms behind the studied phenomenon. We demonstrate the general applicability of our two knowledge integrations by applying them to three architectures that rely on different mechanisms to propagate information between nodes and to update node states. Our implementations are made publicly available. To support these experiments, we release three new datasets of out-of-equilibrium molecules and crystals of various complexities.
published_date 2023-08-31T08:09:22Z
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score 11.056336

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