材料科学
工作(物理)
人工神经网络
电流(流体)
非晶态金属
一般化
数据集
集合(抽象数据类型)
功能(生物学)
机器学习
实验数据
铸造
训练集
人工智能
计算机科学
热力学
冶金
统计
数学
数学分析
物理
进化生物学
生物
程序设计语言
合金
作者
Tao Long,Zhilin Long,Bo Pang,Zhuang Li,Xiaowei Liu
标识
DOI:10.1016/j.mtcomm.2023.105610
摘要
Machine learning (ML) has been extensively studied in predicting the glass-forming ability of bulk metallic glasses (BMGs). Based on the current state of development of BMGs, the reported critical casting diameter (Dmax) data show an imbalance. Nevertheless, almost most of the current literature using ML to predicted Dmax has failed to consider this phenomenon, resulting in generally low prediction accuracy. Only a very small amount of literature deals with this issue only at the data level. In this work, an improved deep neural network (IDNN) model based on the DenseLoss function was proposed at the algorithmic level. The IDNN model has better generalization capability than the currently reported models by obtaining the highest R2 score of 0.841 in the test set. Our work highlights the importance of dealing with the Dmax imbalance problem to address the low accuracy of current ML models in predicting Dmax. This research work will have a significant impact on the discovery of novel BMGs.
科研通智能强力驱动
Strongly Powered by AbleSci AI