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Determining the atomic configuration of an interface is one of the mostimportant issues in materials science research. Although theoreticalsimulations are effective tools, an exhaustive search is computationallyprohibitive due to the high degrees of freedom of the interface structure. Inthe interface structure search, multiple energy surfaces created by a varietyof orientation angles need to be explored, and the necessary computationalcosts for different angles vary substantially owing to significant variationsin the supercell sizes. In this paper, we introduce two machine-learningconcepts, called transfer learning and cost-sensitive search, to theinterface-structure search. As a case study, we demonstrate the effectivenessof our method, called cost-sensitive multi-task Bayesian optimization (CMB),using the fcc-Al [110] tilt grain boundary. Four microscopic parameters, thethree-dimensional rigid body translation, and the number of atomic columns, areoptimized by transferring knowledge of energy surfaces among differentorientation angles. We show that transferring knowledge of different energysurfaces can accelerate the structure search, and that considering the costvariations further improves the total efficiency.

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