2019/03/23 by Tran-Thai Dang, Tien-Lam Pham, Dang, Tran-Thai +8
Computer Science · Materials Science · Mathematics · #Computational Drug Discovery Methods #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Materials Science #X-ray Diffraction in Crystallography #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1903.10867
arxiv created 2019/03/23 · openalex publication_date 2019/03/23 · arxiv updated 2019/03/27 · openalex created_date 2019/04/01 · openalex updated_date 2026/07/28
In this study, we establish a basis for selecting similarity measures when applying machine learning techniques to solve materials science problems. This selection is considered with an emphasis on the distinctiveness between materials that reflect their nature well. We perform a case study with a dataset of rare-earth transition metal crystalline compounds represented using the Orbital Field Matrix descriptor and the Coulomb Matrix descriptor. We perform predictions of the formation energies using k-nearest neighbors regression, ridge regression, and kernel ridge regression. Through detailed analyses of the yield prediction accuracy, we examine the relationship between the characteristics of the material representation and similarity measures, and the complexity of the energy function they can capture. Empirical experiments and theoretical analysis reveal that similarity measures and kernels that minimize the loss of materials distinctiveness improve the prediction performance.