Providing Machine Learning Potentials with High Quality Uncertainty Estimates
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909453520994304 |
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| author | Sumer, Zeynep McDonagh, James L. Fare, Clyde Tadikonda, Ravikanth Zolyomi, Viktor Bray, David Pyzer-Knapp, Edward |
| author_facet | Sumer, Zeynep McDonagh, James L. Fare, Clyde Tadikonda, Ravikanth Zolyomi, Viktor Bray, David Pyzer-Knapp, Edward |
| contents | Computational chemistry has come a long way over the course of several decades, enabling subatomic level calculations particularly with the development of Density Functional Theory (DFT). Recently, machine-learned potentials (MLP) have provided a way to overcome the prevalent time and length scale constraints in such calculations. Unfortunately, these models utilise complex and high dimensional representations, making it challenging for users to intuit performance from chemical structure, which has motivated the development of methods for uncertainty quantification. One of the most common methods is to introduce an ensemble of models and employ an averaging approach to determine the uncertainty. In this work, we introduced Bayesian Neural Networks (BNNs) for uncertainty aware energy evaluation as a more principled and resource efficient method to achieve this goal. The richness of our uncertainty quantification enables a new type of hybrid workflow where calculations can be offloaded to a MLP in a principled manner. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_05250 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Providing Machine Learning Potentials with High Quality Uncertainty Estimates Sumer, Zeynep McDonagh, James L. Fare, Clyde Tadikonda, Ravikanth Zolyomi, Viktor Bray, David Pyzer-Knapp, Edward Computational Physics Chemical Physics Computational chemistry has come a long way over the course of several decades, enabling subatomic level calculations particularly with the development of Density Functional Theory (DFT). Recently, machine-learned potentials (MLP) have provided a way to overcome the prevalent time and length scale constraints in such calculations. Unfortunately, these models utilise complex and high dimensional representations, making it challenging for users to intuit performance from chemical structure, which has motivated the development of methods for uncertainty quantification. One of the most common methods is to introduce an ensemble of models and employ an averaging approach to determine the uncertainty. In this work, we introduced Bayesian Neural Networks (BNNs) for uncertainty aware energy evaluation as a more principled and resource efficient method to achieve this goal. The richness of our uncertainty quantification enables a new type of hybrid workflow where calculations can be offloaded to a MLP in a principled manner. |
| title | Providing Machine Learning Potentials with High Quality Uncertainty Estimates |
| topic | Computational Physics Chemical Physics |
| url | https://arxiv.org/abs/2501.05250 |