Uncertainty Quantification in Atomistic Modeling of Metals and its Effect on Mesoscale and Continuum Modeling A Review

TL;DR

This review discusses the importance and challenges of uncertainty quantification in atomistic modeling of metals, emphasizing its impact on mesoscale and continuum simulations in materials design.

Contribution

It summarizes recent advances in uncertainty quantification methods for atomistic simulations and their integration with mesoscale modeling in materials engineering.

Findings

UQ methods improve reliability of thermodynamic predictions

Bayesian approaches offer probabilistic insights into material properties

Challenges remain in routine application of UQ in atomistic modeling

Abstract

The design of next-generation alloys through the Integrated Computational Materials Engineering (ICME) approach relies on multi-scale computer simulations to provide thermodynamic properties when experiments are difficult to conduct. Atomistic methods such as Density Functional Theory (DFT) and Molecular Dynamics (MD) have been successful in predicting properties of never before studied compounds or phases. However, uncertainty quantification (UQ) of DFT and MD results is rarely reported due to computational and UQ methodology challenges. Over the past decade, studies have emerged that mitigate this gap. These advances are reviewed in the context of thermodynamic modeling and information exchange with mesoscale methods such as Phase Field Method (PFM) and Calculation of Phase Diagrams (CALPHAD). The importance of UQ is illustrated using properties of metals, with aluminum as an example, and highlighting deterministic, frequentist and Bayesian methodologies. Challenges facing routine uncertainty quantification and an outlook on addressing them are also presented.