Dataset and High-Throughput Screening Results for Machine-Learning-Guided Discovery of Relaxor Ferroelectric Ceramics

This repository provides the dataset and screening results used in the study of machine-learning-guided discovery of relaxor ferroelectric ceramics. The repository contains: • ONNL relaxor dataset.xlsx Dataset used for model training and validation. The dataset contains composition, processing conditions, and dielectric properties collected from the literature. • 250804 element_master.xlsx Element descriptor table used to compute high-order physicochemical features for A-site and B-site elements. • HTS_relaxor_pipeline_logProduct_A3_B2_step001.csv High-throughput screening (HTS) results generated by the ensemble neural network model. The screening explores the compositional design space of A3–B2 mixed perovskites with step size 0.01 and applies charge neutrality and tolerance-factor constraints.