Nonnegative tensor decomposition with custom clustering for microphase separation of block copolymers

Statistical Analysis and Data Mining

article

Boian S. Alexandrov¹  Valentin G. Stanev²  Velimir V. Vesselinov³  Kim Ø. Rasmussen¹ 

[1] Theoretical Division, Los Alamos National Laboratory;Department of Materials Science and Engineering, University of Maryland, College Park;Earth and Environmental Sciences Division, Los Alamos National Laboratory

关键词: dimension reduction;    feature extraction;    nonnegative tensor factorization;    phase separation;    unsupervised learning;   

DOI  :  10.1002/sam.11407

学科分类：社会科学、人文和艺术（综合）

来源: John Wiley & Sons, Inc.

【 摘 要 】

High-dimensional datasets are becoming ubiquitous in many applications and therefore unsupervised tensor methods to interrogate them are needed. Here, we report a new unsupervised machine learning (ML) approach (NTFk) based on nonnegative tensor factorization integrated with a custom k-means clustering. We demonstrate the ability of NTFk to extracting temporal and spatial features of phase separation of copolymers as they are modeled by self-consistent field theory. Microphase separation of block copolymers has been extensively studied both experimentally and theoretically. However, the interpretation of computer simulations and/or experimental data, representing temporal and spatial changes of molecular species concentration is still a challenging task. Thus, extracting the phase diagram from simulations or experimental data as well as the interpretation of data requires discernment of the model/experimental parameters (such as, temperature, concentrations, the number of molecular species and the interaction between species) impact on the microphase separation process. An attractive and unique aspect of the introduced ML method is that it ensures the nonnegativity of the extracted latent features. Nonnegativity is an essential constraint needed to obtain interpretable and sparse latent features that are parts-based representation of the data. The custom clustering in NTFk serves to estimate the number of latent features in the data.

【 授权许可】

Unknown