Cross-scale covariance for material property prediction
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912177097539584 |
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| author | Jasperson, Benjamin A. Nikiforov, Ilia Samanta, Amit Zhou, Fei Tadmor, Ellad B. Lordi, Vincenzo Bulatov, Vasily V. |
| author_facet | Jasperson, Benjamin A. Nikiforov, Ilia Samanta, Amit Zhou, Fei Tadmor, Ellad B. Lordi, Vincenzo Bulatov, Vasily V. |
| contents | A simulation can stand its ground against experiment only if its prediction uncertainty is known. The unknown accuracy of interatomic potentials (IPs) is a major source of prediction uncertainty, severely limiting the use of large-scale classical atomistic simulations in a wide range of scientific and engineering applications. Here we explore covariance between predictions of metal plasticity, from 178 large-scale ($\sim 10^8$ atoms) molecular dynamics (MD) simulations, and a variety of indicator properties computed at small-scales ($\leq 10^2$ atoms). All simulations use the same 178 IPs. In a manner similar to statistical studies in public health, we analyze correlations of strength with indicators, identify the best predictor properties, and build a cross-scale ``strength-on-predictors'' regression model. This model is then used to quantify uncertainty over the statistical pool of IPs. Small-scale predictors found to be highly covariant with strength are computed using expensive quantum-accurate calculations and used to predict flow strength, within the uncertainty bounds established in our statistical study. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05146 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cross-scale covariance for material property prediction Jasperson, Benjamin A. Nikiforov, Ilia Samanta, Amit Zhou, Fei Tadmor, Ellad B. Lordi, Vincenzo Bulatov, Vasily V. Materials Science A simulation can stand its ground against experiment only if its prediction uncertainty is known. The unknown accuracy of interatomic potentials (IPs) is a major source of prediction uncertainty, severely limiting the use of large-scale classical atomistic simulations in a wide range of scientific and engineering applications. Here we explore covariance between predictions of metal plasticity, from 178 large-scale ($\sim 10^8$ atoms) molecular dynamics (MD) simulations, and a variety of indicator properties computed at small-scales ($\leq 10^2$ atoms). All simulations use the same 178 IPs. In a manner similar to statistical studies in public health, we analyze correlations of strength with indicators, identify the best predictor properties, and build a cross-scale ``strength-on-predictors'' regression model. This model is then used to quantify uncertainty over the statistical pool of IPs. Small-scale predictors found to be highly covariant with strength are computed using expensive quantum-accurate calculations and used to predict flow strength, within the uncertainty bounds established in our statistical study. |
| title | Cross-scale covariance for material property prediction |
| topic | Materials Science |
| url | https://arxiv.org/abs/2406.05146 |