Data-Efficient Surrogate Models for High-Throughput Density Functional Theory

High-throughput screening of compounds for desirable electronic properties can allow for accelerated discovery and design of materials. Density functional theory (DFT) is the popular approach used for these quantum chemical calculations, but it can be computationally expensive on a large scale. Recently, machine learning methods have gained traction as a supplementation to DFT, with well-trained models achieving similar accuracy as DFT itself. However, training a machine learning model to be accurate and generalizable to unseen materials requires a large amount of training data. This work proposes a method to minimize the need for novel data creation for training by using transfer learning and publicly-available databases, allowing for both data-efficient and accurate machine learning to mitigate the computational cost of DFT