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AI3SD Video: Preserving Structural Motifs in Machine-Learning Approaches to Modeling Water Clusters

AI3SD Video: Preserving Structural Motifs in Machine-Learning Approaches to Modeling Water Clusters
AI3SD Video: Preserving Structural Motifs in Machine-Learning Approaches to Modeling Water Clusters
Chemical structures are naturally viewed as collections of atoms connected through bonds, and graph theory provides a natural tool for capturing that intuition in a concrete mathematical fashion. Over the past several years, graph neural networks have become increasingly popular for modeling chemical systems. To build on this work, the multi-laboratory ExaLearn project, part of the DOE Exascale Computing Project, is developing novel capabilities that combine state-of-the-art machine-learning techniques with high-performance computing to enable the rapid exploration of chemical space on exascale-class systems. Water clusters offer an interesting use case for the development of machine-learning approaches that preserve intermolecular interactions and structural motifs. We apply a dataset of ~5 million hydrogen-bonded water clusters that display interesting long-range structural patterns to explore unique challenges in property prediction and molecular generation.
[1] J. A. Bilbrey, J. P. Heindel, M. Schram, P. Bandyopadyay, S. S. Xantheas, S. Choudhury. “A look inside the black box: Using graph-theoretical descriptors to interpret a Continuous-Filter Convolutional Neural Network (CF-CNN) trained on the global and local minimum energy structures of neutral water clusters,” J. Chem. Phys., 2020, 153, 024302.
[2] S. Choudhury, J. A. Bilbrey, L. Ward, S. S. Xantheas, I. Foster, J. P. Heindel, B. Blaiszik, M. E. Schwarting. “HydroNet: Benchmark Tasks for Preserving Long-range Interactions and Structural Motifs in Predictive and Generative Models for Molecular Data,” Machine Learning and the Physical Sciences workshop at NeurIPS, 2020.
AI, AI3SD Event, Artificial Intelligence, Chemistry, Graphs, Machine Intelligence, Machine Learning, ML, Molecules Discovery, Networks
Bilbrey, Jenna A.
4a48a174-bba6-4099-990c-1fb4e816a159
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Niranjan, Mahesan
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Hooper, Victoria
af1a99f1-7848-4d5c-a4b5-615888838d84
Bilbrey, Jenna A.
4a48a174-bba6-4099-990c-1fb4e816a159
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Hooper, Victoria
af1a99f1-7848-4d5c-a4b5-615888838d84

Bilbrey, Jenna A. (2021) AI3SD Video: Preserving Structural Motifs in Machine-Learning Approaches to Modeling Water Clusters. Kanza, Samantha, Frey, Jeremy G., Niranjan, Mahesan and Hooper, Victoria (eds.) AI3SD Winter Seminar Series, , Online. 18 Nov 2020 - 21 Apr 2021 . (doi:10.5258/SOTON/P0079).

Record type: Conference or Workshop Item (Other)

Abstract

Chemical structures are naturally viewed as collections of atoms connected through bonds, and graph theory provides a natural tool for capturing that intuition in a concrete mathematical fashion. Over the past several years, graph neural networks have become increasingly popular for modeling chemical systems. To build on this work, the multi-laboratory ExaLearn project, part of the DOE Exascale Computing Project, is developing novel capabilities that combine state-of-the-art machine-learning techniques with high-performance computing to enable the rapid exploration of chemical space on exascale-class systems. Water clusters offer an interesting use case for the development of machine-learning approaches that preserve intermolecular interactions and structural motifs. We apply a dataset of ~5 million hydrogen-bonded water clusters that display interesting long-range structural patterns to explore unique challenges in property prediction and molecular generation.
[1] J. A. Bilbrey, J. P. Heindel, M. Schram, P. Bandyopadyay, S. S. Xantheas, S. Choudhury. “A look inside the black box: Using graph-theoretical descriptors to interpret a Continuous-Filter Convolutional Neural Network (CF-CNN) trained on the global and local minimum energy structures of neutral water clusters,” J. Chem. Phys., 2020, 153, 024302.
[2] S. Choudhury, J. A. Bilbrey, L. Ward, S. S. Xantheas, I. Foster, J. P. Heindel, B. Blaiszik, M. E. Schwarting. “HydroNet: Benchmark Tasks for Preserving Long-range Interactions and Structural Motifs in Predictive and Generative Models for Molecular Data,” Machine Learning and the Physical Sciences workshop at NeurIPS, 2020.

Video
AI3SD-Winter-Seminar-Series-GNM-JennaBilbrey - Version of Record
Available under License Creative Commons Attribution.
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More information

Published date: 3 February 2021
Additional Information: Jenna (Bilbrey) Pope is a data scientist at Pacific Northwest National Laboratory. She graduated with her Ph.D. in chemistry from the University of Georgia in 2014, and now uses her knowledge of computational chemistry to explore machine-learning approaches for chemical systems. Code for her projects can be found at https://github.com/jenna1701.
Venue - Dates: AI3SD Winter Seminar Series, , Online, 2020-11-18 - 2021-04-21
Keywords: AI, AI3SD Event, Artificial Intelligence, Chemistry, Graphs, Machine Intelligence, Machine Learning, ML, Molecules Discovery, Networks

Identifiers

Local EPrints ID: 448776
URI: http://eprints.soton.ac.uk/id/eprint/448776
PURE UUID: b721fa1d-99e0-43ce-9e40-bb6152e41f16
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302

Catalogue record

Date deposited: 05 May 2021 16:40
Last modified: 06 May 2021 01:59

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Contributors

Author: Jenna A. Bilbrey
Editor: Samantha Kanza ORCID iD
Editor: Jeremy G. Frey ORCID iD
Editor: Mahesan Niranjan
Editor: Victoria Hooper

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