Stretched non-negative matrix factorization

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Ran Gu, Yevgeny Rakita, Ling Lan, Zach Thatcher, Gabrielle E. Kamm, Daniel O’Nolan, Brennan Mcbride, Allison Wustrow, James R. Neilson, Karena W. Chapman, Qiang Du, Simon J. L. Billinge
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Abstract

A novel algorithm, stretchedNMF, is introduced for non-negative matrix factorization (NMF), accounting for signal stretching along the independent variable’s axis. It addresses signal variability caused by stretching, proving beneficial for analyzing data such as powder diffraction at varying temperatures. This approach provides a more meaningful decomposition, particularly when the component signals resemble those from chemical components in the sample. The stretchedNMF model introduces a stretching factor to accommodate signal expansion, solved using discretization and Block Coordinate Descent algorithms. Initial experimental results indicate that the stretchedNMF model outperforms conventional NMF for datasets exhibiting such expansion. An enhanced version, sparse-stretchedNMF, optimized for powder diffraction data from crystalline materials, leverages signal sparsity for accurate extraction, especially with small stretches. Experimental results showcase its effectiveness in analyzing diffraction data, including success in real-time chemical reaction experiments.

Abstract Image

拉伸非负矩阵因式分解
针对非负矩阵因式分解(NMF)引入了一种新算法--拉伸 NMF,该算法考虑了信号沿自变量轴的拉伸。它解决了拉伸引起的信号变化问题,证明有利于分析不同温度下的粉末衍射等数据。这种方法提供了更有意义的分解,尤其是当成分信号与样品中化学成分的信号相似时。拉伸 NMF 模型引入了一个拉伸因子,以适应信号的扩展,并使用离散化和块坐标下降算法进行求解。初步实验结果表明,对于表现出这种扩展的数据集,拉伸 NMF 模型的性能优于传统 NMF。针对晶体材料粉末衍射数据优化的增强版本--稀疏拉伸 NMF,利用信号稀疏性实现了精确提取,尤其是在小拉伸的情况下。实验结果表明了它在分析衍射数据方面的有效性,包括在实时化学反应实验中的成功应用。
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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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