Integrating adaptive learning with post hoc model explanation and symbolic regression to build interpretable surrogate models

IF 1.8 4区 材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY
Ankita Biswas, Shunshun Liu, Sunidhi Garg, Md Golam Morshed, Hamed Vakili, Avik W. Ghosh, Prasanna V. Balachandran
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引用次数: 0

Abstract

We develop a materials informatics workflow to build an interpretable surrogate model for micromagnetic simulations. Our goal is to predict the energy barrier of a moving isolated skyrmion in rare-earth-free \(\hbox {Mn}_4\)N. Our approach integrates adaptive learning with post hoc model explanation and symbolic regression methods. We discuss an unexplored acquisition function (information condensing active learning) within the adaptive learning loop and compare it with the known standard deviation function for efficient navigation of the search space. Model-agnostic post hoc explanation techniques then uncover trends learned by the trained model, which we then leverage to constrain the expressions used for symbolic regression.

Graphical abstract

Abstract Image

将自适应学习与事后模型解释和符号回归相结合,建立可解释的代用模型
摘要 我们开发了一种材料信息学工作流程,为微磁模拟建立一个可解释的代用模型。我们的目标是预测无稀土(\hbox {Mn}_4\)N中移动的孤立skyrmion的能障。我们的方法将自适应学习与事后模型解释和符号回归方法相结合。我们讨论了自适应学习环路中尚未探索的获取函数(信息浓缩主动学习),并将其与已知的标准偏差函数进行了比较,以实现搜索空间的高效导航。然后,与模型无关的事后解释技术将揭示训练模型学习到的趋势,然后我们利用这些趋势来约束符号回归所用的表达式。
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来源期刊
MRS Communications
MRS Communications MATERIALS SCIENCE, MULTIDISCIPLINARY-
CiteScore
2.60
自引率
10.50%
发文量
166
审稿时长
>12 weeks
期刊介绍: MRS Communications is a full-color, high-impact journal focused on rapid publication of completed research with broad appeal to the materials community. MRS Communications offers a rapid but rigorous peer-review process and time to publication. Leveraging its access to the far-reaching technical expertise of MRS members and leading materials researchers from around the world, the journal boasts an experienced and highly respected board of principal editors and reviewers.
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