Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory
Abstract
An accurate description of information is relevant for a range of problems in atomistic machine learning (ML), such as crafting training sets, performing uncertainty quantification (UQ), or extracting physical insights from large datasets. However, atomistic ML often relies on unsupervised learning or model predictions to analyze information contents from simulation or training data. Here, we introduce a theoretical framework that provides a rigorous, model-free tool to quantify information contents in atomistic simulations. We demonstrate that the information entropy of a distribution of atom-centered environments explains known heuristics in ML potential developments, from training set sizes to dataset optimality. Using this tool, we propose a model-free UQ method that reliably predicts epistemic uncertainty and detects out-of-distribution samples, including rare events in systems such as nucleation. This method provides a general tool for data-driven atomistic modeling and combines efforts in ML, simulations, and physical explainability.
AI-Generated Overview
Here is a brief overview of the extracted text, presented in the requested bullet points format:
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Research Focus: The study focuses on developing a model-free theoretical framework to quantify information completeness, uncertainties, and outliers in atomistic machine learning (ML) simulations using information theory.
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Methodology: The authors introduced a new descriptor that combines atom-centered environments and a novel kernel density estimation approach to calculate information entropy, which is applied to analyze dataset quality and assess uncertainties in ML predictions.
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Results: The method, named QUESTS, reliably predicts epistemic uncertainty, quantifies dataset redundancy, and detects out-of-distribution samples, including rare events during simulations, without needing a trained model.
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Key Contribution(s): The framework provides a rigorous method to connect information theory with atomistic datasets, enhances efficiency in training set construction, and introduces a model-independent approach for uncertainty quantification and outlier detection.
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Significance: The proposed approach addresses key challenges in computational materials science, specifically improving the robustness, interpretability, and reliability of ML-driven simulations. It demonstrates the utility of information theory in quantifying data properties beyond traditional metrics.
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Broader Applications: The findings and methodologies have potential applications in various fields, including materials modeling, computational chemistry, and the broader context of machine learning, where understanding data quality and uncertainties is paramount for predictive accuracy.