Machine-Learning Prediction of Curie Temperature from Chemical Compositions of Ferromagnetic Materials,Journal of Chemical Information and Modeling

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Machine-Learning Prediction of Curie Temperature from Chemical Compositions of Ferromagnetic Materials
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2024-08-07 , DOI: 10.1021/acs.jcim.4c00947
Son Gyo Jung _{1,

2,

3} , Guwon Jung _{1,

3,

4} , Jacqueline M Cole _{1,

2,

3}

Affiliation

Room-temperature ferromagnets are high-value targets for discovery given the ease by which they could be embedded within magnetic devices. However, the multitude of potential interactions among magnetic ions and their surrounding environments renders the prediction of thermally stable magnetic properties challenging. Therefore, it is vital to explore methods that can effectively screen potential candidates to expedite the discovery of novel ferromagnetic materials within highly intricate feature spaces. To this end, we explore machine-learning (ML) methods as a means to predict the Curie temperature (T_c) of ferromagnetic materials by discerning patterns within materials databases. This study emphasizes the importance of feature analysis and selection in ML modeling and demonstrates the efficacy of our gradient-boosted statistical feature-selection workflow for training predictive models. The models are fine-tuned through Bayesian optimization, using features derived solely from the chemical compositions of the materials data, before the model predictions are evaluated against literature values. We have collated ca. 35,000 T_c values and the performance of our workflow is benchmarked against state-of-the-art algorithms, the results of which demonstrate that our methodology is superior to the majority of alternative methods. In a 10-fold cross-validation, our regression model realized an R² of (0.92 ± 0.01), an MAE of (40.8 ± 1.9) K, and an RMSE of (80.0 ± 5.0) K. We demonstrate the utility of our ML model through case studies that forecast T_c values for rare-earth intermetallic compounds and generate magnetic phase diagrams for various chemical systems. These case studies highlight the importance of a systematic approach to feature analysis and selection in enhancing both the predictive capability and interpretability of ML models, while being devoid of potential human bias. They demonstrate the advantages of such an approach over a mere reliance on algorithmic complexity and a black-box treatment in ML-based modeling within the domain of computational materials science.

中文翻译：

根据铁磁材料的化学成分进行机器学习预测居里温度

鉴于室温铁磁体可以轻松嵌入磁性设备中，因此它们是高价值的发现目标。然而，磁性离子与其周围环境之间存在大量潜在的相互作用，使得热稳定磁性的预测变得具有挑战性。因此，探索能够有效筛选潜在候选材料的方法，以加快在高度复杂的特征空间中发现新型铁磁材料至关重要。为此，我们探索机器学习（ML）方法，通过辨别材料数据库中的模式来预测铁磁材料的居里温度（ T _c ）。这项研究强调了机器学习建模中特征分析和选择的重要性，并证明了我们的梯度增强统计特征选择工作流程用于训练预测模型的有效性。在根据文献值评估模型预测之前，使用仅从材料数据的化学成分导出的特征，通过贝叶斯优化对模型进行微调。我们整理了约。 35,000 个T _c值和我们工作流程的性能以最先进的算法为基准，其结果表明我们的方法优于大多数替代方法。在 10 倍交叉验证中，我们的回归模型实现了R ²为 (0.92 ± 0.01)，MAE 为 (40.8 ± 1.9) K，RMSE 为 (80.0 ± 5.0) K。我们通过案例研究展示了 ML 模型的实用性，这些案例研究预测稀土金属间化合物的T _c值并生成各种化学系统的磁相图。这些案例研究强调了特征分析和选择的系统方法对于增强机器学习模型的预测能力和可解释性的重要性，同时避免潜在的人为偏见。他们展示了这种方法相对于计算材料科学领域内基于 ML 的建模中仅依赖算法复杂性和黑盒处理的优势。

更新日期：2024-08-07

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