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Robustness of Local Predictions in Atomistic Machine Learning Models
[Image: see text] Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven simulations with a linear-sc...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10688186/ https://www.ncbi.nlm.nih.gov/pubmed/37948446 http://dx.doi.org/10.1021/acs.jctc.3c00704 |
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author | Chong, Sanggyu Grasselli, Federico Ben Mahmoud, Chiheb Morrow, Joe D. Deringer, Volker L. Ceriotti, Michele |
author_facet | Chong, Sanggyu Grasselli, Federico Ben Mahmoud, Chiheb Morrow, Joe D. Deringer, Volker L. Ceriotti, Michele |
author_sort | Chong, Sanggyu |
collection | PubMed |
description | [Image: see text] Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven simulations with a linear-scaling cost and also allows for the identification and posthoc interpretation of contributions from individual chemical environments and motifs to complicated macroscopic properties. However, even though practical justifications exist for the local decomposition, only the global quantity is rigorously defined. Thus, when the atom-centered contributions are used, their sensitivity to the training strategy or the model architecture should be carefully considered. To this end, we introduce a quantitative metric, which we call the local prediction rigidity (LPR), that allows one to assess how robust the locally decomposed predictions of ML models are. We investigate the dependence of the LPR on the aspects of model training, particularly the composition of training data set, for a range of different problems from simple toy models to real chemical systems. We present strategies to systematically enhance the LPR, which can be used to improve the robustness, interpretability, and transferability of atomistic ML models. |
format | Online Article Text |
id | pubmed-10688186 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-106881862023-12-01 Robustness of Local Predictions in Atomistic Machine Learning Models Chong, Sanggyu Grasselli, Federico Ben Mahmoud, Chiheb Morrow, Joe D. Deringer, Volker L. Ceriotti, Michele J Chem Theory Comput [Image: see text] Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven simulations with a linear-scaling cost and also allows for the identification and posthoc interpretation of contributions from individual chemical environments and motifs to complicated macroscopic properties. However, even though practical justifications exist for the local decomposition, only the global quantity is rigorously defined. Thus, when the atom-centered contributions are used, their sensitivity to the training strategy or the model architecture should be carefully considered. To this end, we introduce a quantitative metric, which we call the local prediction rigidity (LPR), that allows one to assess how robust the locally decomposed predictions of ML models are. We investigate the dependence of the LPR on the aspects of model training, particularly the composition of training data set, for a range of different problems from simple toy models to real chemical systems. We present strategies to systematically enhance the LPR, which can be used to improve the robustness, interpretability, and transferability of atomistic ML models. American Chemical Society 2023-11-10 /pmc/articles/PMC10688186/ /pubmed/37948446 http://dx.doi.org/10.1021/acs.jctc.3c00704 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Chong, Sanggyu Grasselli, Federico Ben Mahmoud, Chiheb Morrow, Joe D. Deringer, Volker L. Ceriotti, Michele Robustness of Local Predictions in Atomistic Machine Learning Models |
title | Robustness of Local
Predictions in Atomistic Machine
Learning Models |
title_full | Robustness of Local
Predictions in Atomistic Machine
Learning Models |
title_fullStr | Robustness of Local
Predictions in Atomistic Machine
Learning Models |
title_full_unstemmed | Robustness of Local
Predictions in Atomistic Machine
Learning Models |
title_short | Robustness of Local
Predictions in Atomistic Machine
Learning Models |
title_sort | robustness of local
predictions in atomistic machine
learning models |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10688186/ https://www.ncbi.nlm.nih.gov/pubmed/37948446 http://dx.doi.org/10.1021/acs.jctc.3c00704 |
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