Correlations between the constituent molecules, crystal structures, and dielectric constants in organic crystals

IF 3.7 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems Pub Date : 2025-03-11 DOI:10.1016/j.chemolab.2025.105376

Yuya Shiraki, Hiromasa Kaneko

引用次数: 0

Abstract

Organic crystals are crystals composed of molecules of organic compounds. Materials made from organic crystals are used in devices, such as capacitors and ferroelectric memories. It is desirable to develop new materials with improved physical properties such as the dielectric constant (DC). However, the relationship between the constituent molecule (CM), crystal structure (CS), and DC of the organic crystals is not clearly understood. In this study, we investigated the relationship between CM, CS, and DC with existing data and machine learning. Using regression analysis, we could construct machine learning models between CM and DC, between CS and DC, and between CM and CS, and could predict DC from CM, DC from CS, and CS from CM using the constructed models.

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来源期刊

Chemometrics and Intelligent Laboratory Systems 工程技术-分析化学

CiteScore

7.50

自引率

7.70%

发文量

169

审稿时长

3.4 months

期刊介绍： Chemometrics and Intelligent Laboratory Systems publishes original research papers, short communications, reviews, tutorials and Original Software Publications reporting on development of novel statistical, mathematical, or computer techniques in Chemistry and related disciplines. Chemometrics is the chemical discipline that uses mathematical and statistical methods to design or select optimal procedures and experiments, and to provide maximum chemical information by analysing chemical data. The journal deals with the following topics: 1) Development of new statistical, mathematical and chemometrical methods for Chemistry and related fields (Environmental Chemistry, Biochemistry, Toxicology, System Biology, -Omics, etc.) 2) Novel applications of chemometrics to all branches of Chemistry and related fields (typical domains of interest are: process data analysis, experimental design, data mining, signal processing, supervised modelling, decision making, robust statistics, mixture analysis, multivariate calibration etc.) Routine applications of established chemometrical techniques will not be considered. 3) Development of new software that provides novel tools or truly advances the use of chemometrical methods. 4) Well characterized data sets to test performance for the new methods and software. The journal complies with International Committee of Medical Journal Editors'' Uniform requirements for manuscripts.