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arXiv 2108.05933cond-mat.mtrl-sci
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基于可解释机器学习的多主元素合金相分类

Phase Classification of Multi-Principal Element Alloys via Interpretable Machine Learning

Kyungtae Lee, Mukil Ayyasamy, Paige Delsa, Timothy Q. Hartnett, Prasanna V. Balachandran

摘要:

有强烈的兴趣,在揭示设计规则,管理作为多主元素合金(MPEAs)的化学组成的函数的各种结构相的形成。在本文中,我们开发了一种机器学习(ML)的方法,建立在集成学习,事后模型解释的黑箱模型和聚类分析的基础上,建立一个定量的化学成分和实验观察阶段的MPEAs之间的关系。我们工作的新颖性源于基于分解方法对ML预测进行实例级(或局部)变量归因分析,然后基于分解结果的k均值聚类分析识别相似实例。我们还使用Ceteris Paribus配置文件来补充故障分析,这些配置文件展示了当所有其他变量的值固定时,模型响应如何作为单个变量的函数变化。局部模型可解释性分析的结果揭示了控制每个阶段形成的变量的关键见解。我们开发的方法是通用的,模型不可知的,并且对于解释黑盒模型所学到的见解是有价值的。开发了一个交互式Web应用程序,以促进模型共享并加速具有目标属性的新型MPEAs的设计

英文摘要:

There is intense interest in uncovering design rules that govern the formation of various structural phases as a function of chemical composition in multi-principal element alloys (MPEAs). In this paper, we develop a machine learning (ML) approach built on the foundations of ensemble learning, post hoc model interpretability of black-box models and clustering analysis to establish a quantitative relationship between the chemical composition and experimentally observed phases of MPEAs. The novelty of our work stems from performing instance-level (or local) variable attribution analysis of ML predictions based on the breakdown method, and then identifying similar instances based on k-means clustering analysis of the breakdown results. We also complement the breakdown analysis with Ceteris Paribus profiles that showcase how the model response changes as a function of a single variable, when the values of all other variables are fixed. Results from local model interpretability analysis uncover key insights into variables that govern the formation of each phase. Our developed approach is generic, model-agnostic, and valuable to explain the insights learned by the black-box models. An interactive web application is developed to facilitate model sharing and accelerate the design of novel MPEAs with targeted properties

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。

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