Papers › Cross-scale covariance for material property prediction
Cross-scale covariance for material property prediction
Benjamin A. Jasperson, Ilia Nikiforov, Amit Samanta, Fei Zhou, Ellad B. Tadmor, Vincenzo Lordi, Vasily V. Bulatov
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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 (∼10⁸ atoms) molecular dynamics (MD) simulations, and a variety of indicator properties computed at small-scales (≤10² 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.
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