Uncertainty Quantification and Propagation in Atomistic Machine Learning
Abstract
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.
AI-Generated Overview
Here’s the requested brief overview of the extracted scientific text, summarized into the identified bullet points:
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Research Focus: The paper reviews methods for uncertainty quantification (UQ) and uncertainty propagation (UP) in atomistic machine learning (ML), emphasizing their importance for reliable predictions in chemical and materials sciences.
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Methodology: The authors categorize existing UQ methods into probabilistic, ensemble, and feature space distance approaches; they assess performance using metrics such as accuracy, precision, calibration, and efficiency, and they explore benchmark studies and techniques for uncertainty propagation in simulation contexts.
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Results: The review identifies that various UQ methods exhibit differing performances across metrics and datasets; the ensemble methods generally show robust performance, while single model approaches like mean-variance estimation also have merits depending on the context.
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Key Contribution(s): The paper provides a comprehensive overview of selected UQ and UP methods, introduces a framework for evaluating their effectiveness, and highlights the interplay between UQ and UP in the modeling pipeline for atomistic ML.
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Significance: This work addresses the critical issue of prediction reliability in atomistic ML, aiming to guide practitioners in selecting appropriate UQ methods based on dataset characteristics and modeling needs, ultimately promoting better-informed decision-making in materials discovery and chemical process design.
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Broader Applications: The insights gained from UQ and UP in atomistic ML have implications for various applications, including materials design, catalysis, and chemical reaction optimization, helping to create more accurate simulations and predictions in these fields.
This summary encapsulates the essential components and contributions of the text, encompassing its main aims, methods, findings, and broader relevance.