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Predicting miscibility in binary compounds: a machine learning and genetic algorithm study

Year: 2024

Type: article

Abstract: This study used atomic-level data and machine learning to predict the miscibility of binary systems, analyzed key factors affecting miscibility, and discovered three new thermodynamically stable phases using a genetic algorithm.

Source: Physical Chemistry Chemical Physics

DOI: 10.1039/d4cp03879g

Authors: Chiwen Feng, Yanwei Liang, Jiaying Sun, Renhai Wang, Huaijun Sun, Huafeng Dong

Institutions: Guangdong University of Technology, Zhengzhou University, Zhejiang A & F University, Jiyang College of Zhejiang A&F University

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Guangdong University of Technology, Basic and Applied Basic Research Foundation of Guangdong Province

Awards: G1581976118, G2158108352, G6495043141, G8715464607

OpenAlex ID: W4405371975

FWCI: 0.095

Cites: 51

Cited by: 1

Related to: 10