使用信息论对原子机器学习中的完整性、不确定性和异常值进行无模型量化
Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory
AI总结：
提出基于信息论的无模型理论框架，利用原子中心环境分布的信息熵量化原子机器学习中的信息内容，实现可靠的认知不确定性预测与分布外样本检测。
摘要：
信息的准确描述与原子机器学习（ML）中的一系列问题相关，例如构建训练集、执行不确定性量化（UQ）或从大型数据集中提取物理见解。然而，原子机器学习通常依赖无监督学习或模型预测来分析来自模拟或训练数据的信息内容。在此，我们引入了一个理论框架，提供了一个严谨的无模型工具来量化原子模拟中的信息内容。我们证明了原子中心环境分布的信息熵解释了ML势能开发中已知的启发式方法，从训练集大小到数据集最优性。使用该工具，我们提出了一种无模型UQ方法，能够可靠地预测认知不确定性并检测分布外样本，包括成核等系统中的罕见事件。该方法为数据驱动的原子建模提供了一个通用工具，并结合了ML、模拟和物理可解释性方面的努力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
