Prediction of interstitial diffusion activation energies of nitrogen, oxygen, boron and carbon in bcc, fcc, and hcp metals using machine learning

Abstract

Study of diffusion in solids is of fundamental importance in understanding the materials properties such as phase transformations, segregation, and corrosion in processing and operations. Reliable diffusion data is crucial in the successful design of materials and manufacturing processes. In this work, we use gradient boosting with decision tree regression, a robust machine learning method, to develop the model for predicting interstitial diffusion activation energies of the light element X (B, C, O, and N) in 54 metals with bcc, fcc and hcp lattices. With the introduction of elastic strain energy, a dominant concept of interstitial diffusion theory, we are able to train the machine learning model achieving high accuracy of R-squared 0.9 without being over-fitted by using 94 impurity – host binary systems reported in the literature. Furthermore, our study reveals that apart from elastic strain energy, the electronic interaction between the impurity and the host (represented by two fundamental parameters in the well-known Miedema model) also plays an important role. By accurately predicting the diffusion activation energies, we report their trends across 4th, 5th and 6th period and Lanthanides series of the host atoms. More importantly, our predicted data can be used to approximate the transport rates in materials for which little or no diffusion data are available, thus accelerating the design of a wide range of new application-specific materials.