Feng Chiwen, Liang Yanwei, Sun Jiaying, Wang Renhai, Sun Huaijun, Dong Huafeng
School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou, 510006, China.
College of Chemistry, Zhengzhou University, Zhengzhou, 450001, China.
Phys Chem Chem Phys. 2025 Feb 19;27(8):4121-4128. doi: 10.1039/d4cp03879g.
The combination of data science and materials informatics has significantly propelled the advancement of multi-component compound synthesis research. This study employs atomic-level data to predict miscibility in binary compounds using machine learning, demonstrating the feasibility of such predictions. We have integrated experimental data from the Materials Project (MP) database and the Inorganic Crystal Structure Database (ICSD), covering 2346 binary systems. We applied a random forest classification model to train the constructed dataset and analyze the key factors affecting the miscibility of binary systems and their significance while predicting binary systems with high synthetic potential. By employing advanced genetic algorithms on the Co-Eu system, we discovered three novel thermodynamically stable phases, CoEu, CoEu, and CoEu. This research offers valuable theoretical insights to guide experimental synthesis endeavors in binary and complex material systems.
数据科学与材料信息学的结合显著推动了多组分化合物合成研究的进展。本研究利用原子级数据，通过机器学习预测二元化合物中的混溶性，证明了此类预测的可行性。我们整合了来自材料项目（MP）数据库和无机晶体结构数据库（ICSD）的实验数据，涵盖2346个二元体系。我们应用随机森林分类模型对构建的数据集进行训练，分析影响二元体系混溶性的关键因素及其在预测具有高合成潜力的二元体系时的重要性。通过在Co-Eu体系上应用先进的遗传算法，我们发现了三个新的热力学稳定相，CoEu、CoEu和CoEu。本研究为指导二元和复杂材料体系的实验合成工作提供了有价值的理论见解。
