Integrating adaptive learning with post hoc model explanation and symbolic regression to build interpretable surrogate models
Year: 2024
Type: article
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 $$\hbox {Mn}_4$$ 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
Source: MRS Communications
Authors: Ankita Biswas, Shunshun Liu, Sunidhi Garg, Md Golam Morshed, Hamed Vakili, Avik W. Ghosh, Prasanna V. Balachandran
Institutions: University of Virginia, University of Nebraska–Lincoln
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,290
OpenAlex ID: W4402357045