Function Decomposition Tree with Causality-First Perspective and Systematic Description of Problems in Materials Informatics
Year: 2022
Type: preprint
Abstract: As interdisciplinary science is flourishing because of materials informatics and additional factors; a systematic way is required for expressing knowledge and facilitating communication between scientists in various fields. A function decomposition tree is such a representation, but domain scientists face difficulty in constructing it. Thus, this study cites the general problems encountered by beginners in generating function decomposition trees and proposes a new function decomposition representation method based on a causality-first perspective for resolution of these problems. The causality-first decomposition tree was obtained from a workflow expressed according to the processing sequence. Moreover, we developed a program that performed automatic conversion using the features of the causality-first decomposition trees. The proposed method was applied to materials informatics to demonstrate the systematic representation of expert knowledge and its usefullness.
Source: arXiv (Cornell University)
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
Awards: G1831662752, G2653377560, Automatic property search incorporating machine learning for high entropy alloy property search, G4307741093, A Design Workbench based on Phases and Facets for Services and Manufacturing Processe, 熱ゆらぎ下におけるマルチグレイン系での保磁力解析法の構築, G4899094766, G5621755428, G6337325905, G6432630292, コンテキストに依存する概念変容のモデル化による目的指向プロセスデザインの深化, G8638384116
OpenAlex ID: W4225405175