Gabriel Joshua J, Paulson Noah H, Duong Thien C, Tavazza Francesca, Becker Chandler A, Chaudhuri Santanu, Stan Marius
Applied Materials Division, Argonne National Laboratory, Lemont, IL 60439, USA.
Energy and Global Security, Argonne National Laboratory, Lemont, IL 60439, USA.
JOM (1989). 2021;73. doi: 10.1007/s11837-020-04436-6.
The design of next-generation alloys through the integrated computational materials engineering (ICME) approach relies on multiscale 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 that mitigate this gap have emerged. These advances are reviewed in the context of thermodynamic modeling and information exchange with mesoscale methods such as the 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.
通过集成计算材料工程（ICME）方法设计下一代合金依赖于多尺度计算机模拟，以便在难以进行实验时提供热力学性质。诸如密度泛函理论（DFT）和分子动力学（MD）等原子方法已成功预测了前所未有的化合物或相的性质。然而，由于计算和不确定性量化（UQ）方法的挑战，DFT和MD结果的不确定性量化很少被报道。在过去十年中，出现了一些弥合这一差距的研究。这些进展在热力学建模以及与中尺度方法（如相场法（PFM）和相图计算（CALPHAD））的信息交换背景下进行了综述。以铝为例，通过金属的性质说明了UQ的重要性，并强调了确定性、频率论和贝叶斯方法。还介绍了常规不确定性量化面临的挑战以及应对这些挑战的展望。
