Molecular dynamic characteristic temperatures for predicting metallic glass forming ability
Molecular dynamic characteristic temperatures for predicting metallic glass forming ability
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DOI：
10.1016/j.commatsci.2021.110877
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发表时间：
2021-09
影响因子：
3.3
通讯作者：
L. Schultz;Ben Afflerbach;I. Szlufarska;D. Morgan
中科院分区：
文献类型：
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作者：
L. Schultz;Ben Afflerbach;I. Szlufarska;D. Morgan
我们探索使用分子动力学的特征温度来预测金属玻璃的形成能力（GFA）方面。从自扩散、粘度和能量的冷却曲线导出的温度被用作GFA的机器学习模型的特征。探索了具有这些特征的多目标和模型组合。首先，我们使用临界铸件厚度的对数，l 〇 g 10（D m a x），作为目标，并在21种成分上训练回归模型。应用3倍交叉验证的21 l o g 10（D m a x）合金显示模型预测值和目标值之间的相关性很弱。第二，合金的GFA通过熔融纺丝或吸铸非晶化行为来量化，其中在合成后显示结晶相的合金被分类为差GFA，而具有纯非晶相的那些被分类为好GFA。然后使用基于决策树的方法（随机森林和梯度提升模型）对二进制GFA分类进行建模，并使用嵌套交叉验证进行评估。以良好的玻璃形成能力为阳性类别的精确-回忆的最大F1得分为0。82±0。01为最好的模型类型。我们还比较了使用特征温度的简单函数作为特征来代替温度本身，并发现预测能力没有统计学上的显著差异。虽然这里开发的模型的预测能力是适度的，但这项工作清楚地表明，人们可以使用分子动力学模拟和机器学习来预测金属玻璃的形成能力。
We explore the use of characteristic temperatures derived from molecular dynamics to predict aspects of metallic Glass Forming Ability (GFA). Temperatures derived from cooling curves of self-diffusion, viscosity, and energy were used as features for machine learning models of GFA. Multiple target and model combinations with these features were explored. First, we use the logarithm of critical casting thickness, l o g 10 (D m a x), as the target and trained regression models on 21 compositions. Application of 3-fold cross-validation on the 21 l o g 10 (D m a x) alloys showed only weak correlation between the model predictions and the target values. Second, the GFA of alloys were quantified by melt-spinning or suction casting amorphization behavior, with alloys that showed crystalline phases after synthesis classified as Poor GFA and those with pure amorphous phases as Good GFA. Binary GFA classification was then modeled using decision tree-based methods (random forest and gradient boosting models) and were assessed with nested-cross validation. The maximum F1 score for the precision–recall with Good Glass Forming Ability as the positive class was 0. 82±0. 01 for the best model type. We also compared using simple functions of characteristic temperatures as features in place of the temperatures themselves and found no statistically significant difference in predictive abilities. Although the predictive ability of the models developed here are modest, this work demonstrates clearly that one can use molecular dynamics simulations and machine learning to predict metal glass forming ability.