论文标题
MODNET-通过特征选择和联合学习对有限材料数据集的准确且可解释的属性预测
MODNet -- accurate and interpretable property predictions for limited materials datasets by feature selection and joint-learning
论文作者
论文摘要
为了对材料属性进行准确的预测，当前的机器学习方法通常需要大量数据，而这些数据通常在实践中不可用。在这项工作中，提出了一个全方位的框架，该框架依赖于前馈神经网络，选择身体上的特征以及适用的联合学习。除了在训练时间方面更快，这种方法被证明超过了小型数据集上的当前图形网络模型。特别是，预测305 K晶体处的振动熵的平均绝对测试误差为0.009 meV/k/Atom（比以前的研究低四倍）。此外，与单目标学习相比，联合学习可以减少测试误差，并可以立即预测多个属性，例如温度函数。最后，选择算法突出了最重要的特征，从而有助于理解潜在的物理学。
In order to make accurate predictions of material properties, current machine-learning approaches generally require large amounts of data, which are often not available in practice. In this work, an all-round framework is presented which relies on a feedforward neural network, the selection of physically-meaningful features and, when applicable, joint-learning. Next to being faster in terms of training time, this approach is shown to outperform current graph-network models on small datasets. In particular, the vibrational entropy at 305 K of crystals is predicted with a mean absolute test error of 0.009 meV/K/atom (four times lower than previous studies). Furthermore, joint-learning reduces the test error compared to single-target learning and enables the prediction of multiple properties at once, such as temperature functions. Finally, the selection algorithm highlights the most important features and thus helps understanding the underlying physics.
