Machine learning (ML) offers promising new approaches to tackle complex problems and has been increasingly adopted in chemical and materials sciences. Broadly speaking, ML models employ generic mathematical functions and attempt to learn essential physics and chemistry from a large amount of data. Consequently, because of the limited physical or chemical principles in the functional form, the reliability of the predictions is oftentimes not guaranteed, particularly for data far out of distribution. It is critical to quantify the uncertainty in model predictions and understand how the uncertainty propagates to downstream chemical and materials applications. Herein, we review existing uncertainty quantification (UQ) and uncertainty propagation (UP) methods for atomistic ML under a united framework of probabilistic modeling. We first categorize the UQ methods, with the aim to elucidate the similarities and differences between them. We also discuss performance metrics to evaluate the accuracy, precision, calibration, and efficiency of the UQ methods and techniques for model recalibration. With these metrics, we survey existing benchmark studies of the UQ methods using molecular and materials datasets. Furthermore, we discuss UP methods to propagate the uncertainty obtained from ML models in widely used materials and chemical simulation techniques, such as molecular dynamics and microkinetic modeling. We also provide remarks on the challenges and future opportunities of UQ and UP in atomistic ML.
Comparative study of ensemble-based uncertainty quantification methods for neural network interatomic potentials
Brigham Young University（杨百翰大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Minnesota（明尼苏达大学）；Cross Stream Consulting（Cross Stream 咨询公司）
High-quality, high-information datasets for universal atomistic machine learning
École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；MPI for the Structure and Dynamics of Matter（马普物质结构与动力学研究所）；University of Cambridge（剑桥大学）；BASF SE（巴斯夫股份公司）
Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials
Department of Materials Science and Engineering, Faculty of Mechanical Engineering, Delft University of Technology（材料科学与工程系，机械工程学院，代尔夫特理工大学）；J. Mike Walker’66 Department of Mechanical Engineering, Texas A&M University（J. Mike Walker’66机械工程系，德克萨斯农工大学）；Department of Materials Science & Engineering, Texas A&M University（材料科学与工程系，德克萨斯农工大学）
Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials
Leitat Technological Center（Leitat技术中心）；Universitat de Barcelona（巴塞罗那大学）；Utrecht University（乌得勒支大学）；ICREA (Institucio Catalana de Recerca i Estudis Avançats)（加泰罗尼亚研究与高等研究院）
Uncertainty Propagation in a Multiscale CALPHAD-Reinforced Elastochemical Phase-field Model
ICME方法通过建立定量的工艺-结构-性能关系，为材料设计提供决策支持。然而，必须通过在ICME模型链中建立不确定性界限来实现对决策支持的信心。然而，计算材料科学中不确定性的量化和传播仍然是计算材料科学方法的一个相当未探索的方面。此外，传统的不确定性传播框架往往是有限的情况下，计算昂贵的模拟。一个相当常见和重要的模型链是基于CALPHAD的相稳定性热力学模型与相场模型耦合的微观结构演化模型。在这些情况下，不确定性的传播是具有挑战性的，不仅是由于纯粹的模拟计算成本，而且还因为输入空间的高维数。在这项工作中，我们提出了一个框架的量化和传播的不确定性，在基于CALPHAD的弹性化学相场模型。我们通过研究Mg $_2$（Si $_x$ Sn $_{1-x}$）热电材料的微观结构演变来激发我们的工作。我们首先进行了马尔可夫链蒙特卡洛为基础的推断的CALPHAD模型参数的伪二进制系统，然后使用先进的采样方案传播的不确定性在高维模拟输入空间。通过高通量相场模拟，我们生成了200，000个合成微结构的时间序列，并使用机器学习方法来了解传播的不确定性对所研究系统的微结构景观的影响。微观结构数据集已在开放相场微观结构数据库（OPMD）中整理，可在\href{http://microstructures.net}{http://microstructures.net}获得。
