Application of Machine Learning to Sporadic Experimental Data for Understanding Epitaxial Strain Relaxation

Abstract

Understanding epitaxial strain relaxation is one of the key challenges in functional thin films with strong structure-property relation. Herein, we employ an emerging data analytics approach to quantitatively evaluate the underlying relationships between critical thickness (hc) of strain relaxation and various physical and chemical features, despite the sporadic experimental data points available. First, we have collected and refined reported hc of perovskite oxide thin film/substrate system to construct a consistent sub-dataset which captures a common trend among the varying experimental details. Then, we employ correlation analyses and feature engineering to find the most relevant feature set which include Poisson's ratio and lattice mismatch. With the insight offered by correlation analyses and feature engineering, machine learning (ML) models have been trained to deduce a decent accuracy, which has been further validated experimentally. the demonstrated framework is expected to be efficiently extended to the other classes of thin films in understanding hc.

24 pages, 5 figures

References in corpus (5)

• Predicting the thermodynamic stability of perovskite oxides using machine learning models
• Machine Learning Phases of Strongly Correlated Fermions
• Coupling Physics in Machine Learning to Predict Properties of High-temperatures Alloys
• Strain-induced tuning of the electronic Coulomb interaction in 3d transition metal oxide perovskites
• Strain-induced atomic-scale building blocks for ferromagnetism in epitaxial LaCoO3