LTAU-FF: Loss Trajectory Analysis for Uncertainty in atomistic Force Fields
Model ensembles are effective tools for estimating prediction uncertainty in deep learning atomistic force fields. However, their widespread adoption is hindered by high computational costs and overconfident error estimates. In this work, we address these challenges by leveraging distributions of pe...
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| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/adb4b9 |
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| author | Joshua A Vita Amit Samanta Fei Zhou Vincenzo Lordi |
| author_facet | Joshua A Vita Amit Samanta Fei Zhou Vincenzo Lordi |
| author_sort | Joshua A Vita |
| collection | DOAJ |
| description | Model ensembles are effective tools for estimating prediction uncertainty in deep learning atomistic force fields. However, their widespread adoption is hindered by high computational costs and overconfident error estimates. In this work, we address these challenges by leveraging distributions of per-sample errors obtained during training and employing a distance-based similarity search in the model latent space. Our method, which we call LTAU (Loss Trajectory Analysis for Uncertainty), efficiently estimates the full probability distribution function of errors for any test point using the logged training errors, achieving speeds that are 2–3 orders of magnitudes faster than typical ensemble methods and allowing it to be used for tasks where training or evaluating multiple models would be infeasible. We apply LTAU towards estimating parametric uncertainty in atomistic force fields ( LTAU-FF ), demonstrating that it produces well-calibrated confidence intervals and predicts errors that correlate strongly with the true errors for data near the training domain. Furthermore, we show that the errors predicted by LTAU-FF can be used in practical applications for detecting out-of-domain data, tuning model performance, and predicting failure during simulations. We believe that LTAU will be a valuable tool for uncertainty quantification in atomistic force fields and is a promising method that should be further explored in other domains of machine learning. |
| format | Article |
| id | doaj-art-6e4e4dcc301a4e0c8b240a57ef087068 |
| institution | OA Journals |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-6e4e4dcc301a4e0c8b240a57ef0870682025-08-20T02:14:26ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016101504810.1088/2632-2153/adb4b9LTAU-FF: Loss Trajectory Analysis for Uncertainty in atomistic Force FieldsJoshua A Vita0https://orcid.org/0000-0001-9191-055XAmit Samanta1https://orcid.org/0000-0003-3620-987XFei Zhou2https://orcid.org/0000-0001-9659-4648Vincenzo Lordi3https://orcid.org/0000-0003-2415-4656Materials Science Division Lawrence Livermore National Laboratory Livermore , Livermore, CA 94550, United States of AmericaPhysics Division Lawrence Livermore National Laboratory Livermore , Livermore, CA 94550, United States of AmericaPhysics Division Lawrence Livermore National Laboratory Livermore , Livermore, CA 94550, United States of AmericaMaterials Science Division Lawrence Livermore National Laboratory Livermore , Livermore, CA 94550, United States of AmericaModel ensembles are effective tools for estimating prediction uncertainty in deep learning atomistic force fields. However, their widespread adoption is hindered by high computational costs and overconfident error estimates. In this work, we address these challenges by leveraging distributions of per-sample errors obtained during training and employing a distance-based similarity search in the model latent space. Our method, which we call LTAU (Loss Trajectory Analysis for Uncertainty), efficiently estimates the full probability distribution function of errors for any test point using the logged training errors, achieving speeds that are 2–3 orders of magnitudes faster than typical ensemble methods and allowing it to be used for tasks where training or evaluating multiple models would be infeasible. We apply LTAU towards estimating parametric uncertainty in atomistic force fields ( LTAU-FF ), demonstrating that it produces well-calibrated confidence intervals and predicts errors that correlate strongly with the true errors for data near the training domain. Furthermore, we show that the errors predicted by LTAU-FF can be used in practical applications for detecting out-of-domain data, tuning model performance, and predicting failure during simulations. We believe that LTAU will be a valuable tool for uncertainty quantification in atomistic force fields and is a promising method that should be further explored in other domains of machine learning.https://doi.org/10.1088/2632-2153/adb4b9machine learninginteratomic potentialforce fielduncertainty quantificationensembles |
| spellingShingle | Joshua A Vita Amit Samanta Fei Zhou Vincenzo Lordi LTAU-FF: Loss Trajectory Analysis for Uncertainty in atomistic Force Fields Machine Learning: Science and Technology machine learning interatomic potential force field uncertainty quantification ensembles |
| title | LTAU-FF: Loss Trajectory Analysis for Uncertainty in atomistic Force Fields |
| title_full | LTAU-FF: Loss Trajectory Analysis for Uncertainty in atomistic Force Fields |
| title_fullStr | LTAU-FF: Loss Trajectory Analysis for Uncertainty in atomistic Force Fields |
| title_full_unstemmed | LTAU-FF: Loss Trajectory Analysis for Uncertainty in atomistic Force Fields |
| title_short | LTAU-FF: Loss Trajectory Analysis for Uncertainty in atomistic Force Fields |
| title_sort | ltau ff loss trajectory analysis for uncertainty in atomistic force fields |
| topic | machine learning interatomic potential force field uncertainty quantification ensembles |
| url | https://doi.org/10.1088/2632-2153/adb4b9 |
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