Cavendish Laboratory, Department of Physics, University of Cambridge, J. J. Thomson Avenue, Cambridge, CB3 0HE, U.K.
ISIS Neutron and Muon Source, STFC Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0QX, U.K.
J Chem Inf Model. 2024 Aug 26;64(16):6388-6409. doi: 10.1021/acs.jcim.4c00947. Epub 2024 Aug 7.
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 () 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 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 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 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.
室温铁磁体是高价值的发现目标，因为它们可以很容易地嵌入磁性器件中。然而，由于磁离子及其周围环境之间存在多种潜在相互作用，因此预测热稳定的磁性性质具有挑战性。因此，探索能够有效筛选潜在候选物的方法对于在高度复杂的特征空间中加速新型铁磁材料的发现至关重要。为此，我们探索了机器学习（ML）方法，通过在材料数据库中识别模式来预测铁磁材料的居里温度（Tc）。本研究强调了在 ML 建模中进行特征分析和选择的重要性，并展示了我们基于梯度提升的统计特征选择工作流程在训练预测模型方面的有效性。通过仅使用材料数据的化学成分衍生的特征，通过贝叶斯优化对模型进行微调，然后根据文献值评估模型预测。我们已经收集了约 35000 个 Tc 值，并且我们的工作流程的性能与最先进的算法进行了基准测试，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（0.92±0.01）的 r2 值、（40.8±1.9）K 的 MAE 值和（80.0±5.0）K 的 RMSE 值。我们通过案例研究展示了我们的 ML 模型的实用性，这些案例研究预测了稀土金属间化合物的 Tc 值，并为各种化学系统生成了磁相图。这些案例研究强调了在增强 ML 模型的预测能力和可解释性的同时，对特征分析和选择采用系统方法的重要性，同时避免了潜在的人为偏见。它们表明，与仅仅依赖于算法复杂性和 ML 建模中的黑盒处理相比，这种方法在计算材料科学领域具有优势。
