-
第一性原理、热力学模拟等传统的材料计算方法在高熵合金的设计中多用于合金相的预测, 同时会耗费巨大的计算资源. 本文以性能为导向, 选用机器学习的算法建立了一个高熵合金硬度预测模型, 并将机器学习与固溶体强化的物理模型相结合, 使用遗传算法筛选出最具有代表性的3个特征参数, 利用这3个特征构建的随机森林模型, 其 R 2达到了0.9416, 对高熵合金的硬度取得了较好的预测效果. 本文选用的机器学习算法和3个材料特征在固溶体强化性质方面也有一定的预测效果. 针对随机森林可解释性较差的问题, 本文还利用SHAP可解释机器学习方法挖掘了机器学习模型的内在推理逻辑.Traditional material calculation methods, such as first principles and thermodynamic simulations, have accelerated the discovery of new materials. However, these methods are difficult to construct models flexibly according to various target properties. And they will consume many computational resources and the accuracy of their predictions is not so high. In the last decade, data-driven machine learning techniques have gradually been applied to materials science, which has accumulated a large quantity of theoretical and experimental data. Machine learning is able to dig out the hidden information from these data and help to predict the properties of materials. The data in this work are obtained from the published references. And several performance-oriented algorithms are selected to build a prediction model for the hardness of high entropy alloys. A high entropy alloy hardness dataset containing 19 candidate features is trained, tested, and evaluated by using an ensemble learning algorithm: a genetic algorithm is selected to filter the 19 candidate features to obtain an optimized feature set of 8 features; a two-stage feature selection approach is then combined with a traditional solid solution strengthening theory to optimize the features, three most representative feature parameters are chosen and then used to build a random forest model for hardness prediction. The prediction accuracy achieves an R 2value of 0.9416 by using the 10-fold cross-validation method. To better understand the prediction mechanism, solid solution strengthening theory of the alloy is used to explain the hardness difference. Further, the atomic size, electronegativity and modulus mismatch features are found to have very important effects on the solid solution strengthening of high entropy alloys when genetic algorithms are used for implementing the feature selection. The machine learning algorithm and features are further used for predicting solid solution strengthening properties, resulting in an R 2of 0.8811 by using the 10-fold cross-validation method. These screened-out parameters have good transferability for various high entropy alloy systems. In view of the poor interpretability of the random forest algorithm, the SHAP interpretable machine learning method is used to dig out the internal reasoning logic of established machine learning model and clarify the mechanism of the influence of each feature on hardness. Especially, the valence electron concentration is found to have the most significant weakening effect on the hardness of high entropy alloys.
[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] -
材料特征 公式 材料特征 公式 材料特征 公式 Tm,Ec, VEC, $ e/a $,
$ E $, $ G $(由$ \alpha $表示)$ \displaystyle\sum _{i=1}^{n}{c}_{i}{\alpha }_{i} $ ΔSmix $ -R\displaystyle\sum _{i=1}^{n}{c}_{i}{\rm{l}}{\rm{n}}\left({c}_{i}\right) $ $ {w}^{6} $ $ {\left(\displaystyle\sum _{i=1}^{n}{c}_{i}{w}_{i}\right)}^{6} $ $ {\text{δ}}G $ $ \sqrt{\displaystyle\sum _{i=1}^{n}{c}_{i}{\left(1-\frac{{G}_{i}}{G}\right)}^{2}} $ ΔGmix $ {{{\Delta }}H}_{{\rm{m}}{\rm{i}}{\rm{x}}}-{T}_{{\rm{m}}}{{{\Delta }}S}_{{\rm{m}}{\rm{i}}{\rm{x}}} $ μ $ \dfrac{1}{2}E{\text{δ}}r $ $ {\text{δ}}r $ $ \sqrt{\displaystyle\sum _{i=1}^{n}{c}_{i}{\left(1-\frac{{r}_{i}}{r}\right)}^{2}} $ $ {{\Delta }}\chi $ $ \sqrt{\displaystyle\sum _{i=1}^{n}{c}_{i}{\left(\chi -{\chi }_{i}\right)}^{2}} $ $ \varOmega $ Tm$ \dfrac{{{{\Delta }}S}_{{\rm{m}}{\rm{i}}{\rm{x}}}}{{{{\Delta }}H}_{{\rm{m}}{\rm{i}}{\rm{x}}}} $ $ \gamma $ $ \dfrac{1-\sqrt{\dfrac{{\left(r+{r}_{{\rm{m}}{\rm{i}}{\rm{n}}}\right)}^{2}-{r}^{2}}{{\left(r+{r}_{{\rm{m}}{\rm{i}}{\rm{n}}}\right)}^{2}}}}{1-\sqrt{\dfrac{{\left(r+{r}_{{\rm{m}}{\rm{a}}{\rm{x}}}\right)}^{2}-{r}^{2}}{{\left(r+{r}_{{\rm{m}}{\rm{a}}{\rm{x}}}\right)}^{2}}}} $ $ A $ $ G{\text{δ}}{{r}}\dfrac{1+\mu }{1-\mu } $ $ \varLambda $ $ \dfrac{{{{\Delta }}S}_{{\rm{m}}{\rm{i}}{\rm{x}}}}{{\text{δ}}r} $ ΔHmix $ 4\displaystyle\sum _{i=1, j > i}^{n}{c}_{i}{c}_{j}{H}_{i{\text{-}}j}^{{\rm{m}}{\rm{i}}{\rm{x}}} $ $ F $ $ \dfrac{2 G}{1-\mu } $ 
下载:
导出CSV
算法 超参数 SVM-rbf gamma = 1×10–7,C= 200 RF max_depth = 6, min_samples_leaf = 1, min_samples_split = 2, n_estimators = 50 XGBoost gamma = 0.1, learning_rate = 0.1, max_depth = 12, n_estimators = 100, reg_alpha = 0, reg_lambda = 0.5 ANN max_iter = 210000, hidden_layer_sizes = 16, solver = 'adam', activation = 'relu',
alpha = 0.01Lasso alpha = 1, max_iter = 10000 Ridge alpha = 0.1, max_iter = 10000 下载:
导出CSV
算法 优化特征组 RMSE GA γ, Δχ, VEC,F,Ω,e/a,E, δG 64.09 SFS δr,Ec, VEC, ΔHmix,Ω, E,G 67.32 SBS Δχ,Ec, VEC,Λ,w, F, δG 67.00 RFE δr,Ec, VEC, ΔSmix,Ω, Λ,E,
μ, w, G,F,A,δG69.67 RF δr, VECF,Λ,w,δG, μ, G,
A, Ec,Ω,ΔSmix, ΔHmix68.99 下载:
导出CSV
-
[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40]
下载:
计量
- 文章访问数:3185
- PDF下载量:141
- 被引次数:0