论文标题
一种预测三元固体解决方案的吉布斯自由能的神经网络方法
A Neural Network Approach to Predict Gibbs Free Energy of Ternary Solid Solutions
论文作者
论文摘要
我们使用神经网络（NNS）提出了一种以数据为中心的深度学习（DL）方法，以预测三元固体溶液的热力学。我们探索如何使用从Calphad数据库计算的Gibbs自由能的数据集对NN进行训练，以预测三元系统作为组成和温度的函数。我们在226个二进制物中选择了FCC固体溶液相的能量学，由23个元素在11个不同的温度下组成，以证明可行性。本研究中包含的二进制数据点的数量为102,000。我们选择六个三元元素来增强二进制数据集，以调查其对NN预测准确性的影响。我们检查了数据采样对NNS对选定三元系统的预测准确性的敏感性。可以预计，可以通过将高级描述符集成到元素组成和更精心策划的训练数据集以提高预测准确性和适用性，从而进一步提高当前DL工作流程。
We present a data-centric deep learning (DL) approach using neural networks (NNs) to predict the thermodynamics of ternary solid solutions. We explore how NNs can be trained with a dataset of Gibbs free energies computed from a CALPHAD database to predict ternary systems as a function of composition and temperature. We have chosen the energetics of the FCC solid solution phase in 226 binaries consisting of 23 elements at 11 different temperatures to demonstrate the feasibility. The number of binary data points included in the present study is 102,000. We select six ternaries to augment the binary dataset to investigate their influence on the NN prediction accuracy. We examine the sensitivity of data sampling on the prediction accuracy of NNs over selected ternary systems. It is anticipated that the current DL workflow can be further elevated by integrating advanced descriptors beyond the elemental composition and more curated training datasets to improve prediction accuracy and applicability.
