粘度
人工神经网络
多层感知器
感知器
共晶体系
深共晶溶剂
一般化
计算机科学
近似误差
状态方程
热力学
人工智能
算法
材料科学
数学
物理
数学分析
合金
复合材料
作者
Liu‐Ying Yu,Gao‐Peng Ren,Xiao‐Jing Hou,Ke‐Jun Wu,Yuchen He
标识
DOI:10.1021/acscentsci.2c00157
摘要
of 0.9805). Compared with the traditional machine learning methods, the TSTiNet has better generalization ability and dramatically reduces the maximum relative deviation of prediction under the constraints of the thermodynamic formulation. It requires only the structural information on DESs and is the most accurate and reliable model available for DESs viscosity prediction.
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