Knowledge-Transfer based Cost-effective Search for Interface Structures: A Case Study on fcc-Al [110] Tilt Grain Boundary

TL;DR

This paper introduces a cost-sensitive multi-task Bayesian optimization method leveraging transfer learning to efficiently determine interface structures, demonstrated on fcc-Al [110] tilt grain boundaries, reducing computational costs significantly.

Contribution

The paper presents a novel combination of transfer learning and cost-sensitive search within Bayesian optimization for interface structure determination, improving efficiency over traditional methods.

Findings

Transfer learning accelerates structure search.

Cost-sensitive approach reduces computational costs.

Method effective on fcc-Al [110] tilt grain boundary.

Abstract

Determining the atomic configuration of an interface is one of the most important issues in materials science research. Although theoretical simulations are effective tools, an exhaustive search is computationally prohibitive due to the high degrees of freedom of the interface structure. In the interface structure search, multiple energy surfaces created by a variety of orientation angles need to be explored, and the necessary computational costs for different angles vary substantially owing to significant variations in the supercell sizes. In this paper, we introduce two machine-learning concepts, called transfer learning and cost-sensitive search, to the interface-structure search. As a case study, we demonstrate the effectiveness of our method, called cost-sensitive multi-task Bayesian optimization (CMB), using the fcc-Al [110] tilt grain boundary. Four microscopic parameters, the three-dimensional rigid body translation, and the number of atomic columns, are optimized by transferring knowledge of energy surfaces among different orientation angles. We show that transferring knowledge of different energy surfaces can accelerate the structure search, and that considering the cost variations further improves the total efficiency.