Significant advances in theory, simulation tools, advanced computing infrastructure, and experimental frameworks have enabled the field of materials science to become increasingly reliant on computer simulations. Theory-grounded computational models provide a better understanding of observed materials phenomena. At the same time, computational tools constitute an important ingredient of any framework that seeks to accelerate the materials development cycle. While simulations keep increasing in sophistication, formal frameworks for the quantification, propagation, and management of their uncertainties are required. Uncertainty analysis is fundamental to any effort to validate and verify simulations, which is often overlooked. Likewise, no simulation-driven materials design effort can be done with any level of robustness without properly accounting for the uncertainty in the predictions derived from the computational models. Here, we review some of the most recent works that have focused on the analysis, quantification, propagation, and management of uncertainty in computational materials science and ICME-based simulation-assisted materials design. Modern concepts of efficient uncertainty quantification and propagation, multi-scale/multi-level uncertainty analysis, model selection as well as model fusion are also discussed. While the topic remains relatively unexplored, there have been significant advances that herald an increased sophistication in the approaches followed for model validation and verification and model-based decision support.

中文翻译：

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计算材料科学和模拟辅助材料设计中的不确定性量化和传播

理论，仿真工具，先进的计算基础结构和实验框架的重大进步使材料科学领域变得越来越依赖于计算机仿真。基于理论的计算模型可以更好地了解观察到的材料现象。同时，计算工具是任何旨在加快材料开发周期的框架的重要组成部分。尽管模拟的复杂性不断提高，但仍需要用于量化，传播和管理其不确定性的正式框架。不确定性分析对于任何验证和验证仿真的工作都是至关重要的，而这常常被忽略。同样 没有适当考虑到计算模型得出的预测中的不确定性，就不可能在任何级别的鲁棒性下进行由仿真驱动的材料设计工作。在这里，我们回顾了一些最新的工作，这些工作集中在计算材料科学和基于ICME的模拟辅助材料设计中的不确定性的分析，量化，传播和管理上。还讨论了有效不确定性量化和传播，多尺度/多层次不确定性分析，模型选择以及模型融合的现代概念。尽管该主题仍未得到充分探讨，但已经取得了重大进展，预示着模型验证和验证以及基于模型的决策支持所采用的方法将变得越来越复杂。