Corrigendum to “Rapid prediction of phonon density of states by crystal attention graph neural network and high-throughput screening of candidate substrates for wide bandgap electronic cooling” [Mater. Today Phys. 50 (2025) 101632]

IF 10 2区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Mohammed Al-Fahdi , Changpeng Lin , Chen Shen , Hongbin Zhang , Ming Hu
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引用次数: 0
晶体注意力图神经网络对声子态密度的快速预测以及宽带隙电子制冷候选基底的高通量筛选》[《今日材料物理学》50 (2025) 101632]勘误表
对于已发表论文中图1-3在论文论证阶段错误上传,作者深表遗憾。现在更正的是正确的图1-3,这里显示的图与审稿人在审稿过程中审稿的图完全相同,应该包含在当前发表的论文中。因此,作者要求修改出版物中的图1-3。对于可能造成的任何不便,我们深表歉意。请注意,已发表论文中的所有图片标题和文本都不会改变。下载:下载高分辨率图片(688KB)下载:下载全尺寸图片1。。晶体注意图神经网络(CATGNN)模型详细框架。晶体结构以图形形式表示,在一定的截止点内,具有节点特征和节点属性以及边缘特征和边缘属性的周期性边界条件。卷积、门控、多头增强晶体注意层和单头原子注意层的简单解释和简要公式。通过正向和反向传播输出晶体结构的预测总声子DOS,以尽量减少总DOS的损失。下载:下载高分辨率图片(737KB)下载:下载全尺寸图片2. a)对数据集中所有62个元素进行规范化和平衡的训练、验证和测试分割,以确保数据集中的平衡。b)整个数据集的晶格常数(A, b, c)的直方图。c)训练和验证损失曲线分别为蓝色和黄色,每历元最多150次。下载:下载高分辨率图片(356KB)下载:下载全尺寸图片3. 随机选择材料对训练好的CATGNN模型进行验证。(左图)总声子DOS测试集中的MSE四分位数。(右图)黑色代表DFT计算的材料的“真实”总声子DOS,而其他颜色代表MSE四分位数区域内的预测总声子DOS:浅绿色(0-0.25)、蓝色(0.25-0.5)、黄色(0.5-0.75)和浅红色(0.75-1)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Materials Today Physics
Materials Today Physics Materials Science-General Materials Science
CiteScore
14.00
自引率
7.80%
发文量
284
审稿时长
15 days
期刊介绍: Materials Today Physics is a multi-disciplinary journal focused on the physics of materials, encompassing both the physical properties and materials synthesis. Operating at the interface of physics and materials science, this journal covers one of the largest and most dynamic fields within physical science. The forefront research in materials physics is driving advancements in new materials, uncovering new physics, and fostering novel applications at an unprecedented pace.
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