Xue Si-Da, Hong Qi-Jun
School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ 85287, USA.
Materials (Basel). 2024 Aug 23;17(17):4176. doi: 10.3390/ma17174176.
Predicting material properties has always been a challenging task in materials science. With the emergence of machine learning methodologies, new avenues have opened up. In this study, we build upon our recently developed graph neural network (GNN) approach to construct models that predict four distinct material properties. Our graph model represents materials as element graphs, with chemical formulas serving as the only input. This approach ensures permutation invariance, offering a robust solution to prior limitations. By employing bootstrap methods to train this individual GNN, we further enhance the reliability and accuracy of our predictions. With multi-task learning, we harness the power of extensive datasets to boost the performance of smaller ones. We introduce the inaugural version of the Materials Properties Prediction (MAPP) framework, empowering the prediction of material properties solely based on chemical formulas.
预测材料性能一直是材料科学中的一项具有挑战性的任务。随着机器学习方法的出现，新的途径已经开辟。在本研究中，我们基于我们最近开发的图神经网络（GNN）方法构建模型，以预测四种不同的材料性能。我们的图模型将材料表示为元素图，仅将化学式作为输入。这种方法确保了置换不变性，为先前的局限性提供了一个稳健的解决方案。通过采用自助法训练这个单独的GNN，我们进一步提高了预测的可靠性和准确性。通过多任务学习，我们利用大量数据集的力量来提高较小数据集的性能。我们推出了材料性能预测（MAPP）框架的首个版本，实现仅基于化学式对材料性能进行预测。
