GAP model parameter files for "Combining phonon accuracy with high transferability in Gaussian approximation potential models"

GAP model parameter files to accompany the publication "Combining phonon accuracy with high transferability in Gaussian approximation potential models" by Janine George, Geoffroy Hautier, Albert P. Bartók, Gábor Csányi, and Volker L. Deringer All models are defined by the main parameter file "gp_iter6C.xml" and the associated file "gp_iter6C.xml.sparseX.GAP_00001", where "GAP_0000" is a placeholder for the unique identifier of the potential (also given in the XML header), and the trailing "1" indicates that only one set of descriptor (here, SOAP) parameters is given. The directories in this dataset follow the figures in the publication for which the respective potentials have been first used. Fig_2/only_random/M_1000<br> Fig_2/only_random/M_3000<br> Fig_2/only_random/M_5000<br> Fig_2/only_random/M_7000<br> Fig_2/only_random/M_9000 Fig_2/only_individual/M_1000<br> Fig_2/only_individual/M_3000<br> Fig_2/only_individual/M_5000<br> Fig_2/only_individual/M_7000<br> Fig_2/only_individual/M_9000 Fig_2/combined/M_1000<br> Fig_2/combined/M_3000<br> Fig_2/combined/M_5000<br> Fig_2/combined/M_7000<br> Fig_2/combined/M_9000 Fig_4/only_individual/f_0.1000<br> Fig_4/only_individual/f_0.0100<br> Fig_4/only_individual/f_0.0010<br> Fig_4/only_individual/f_0.0001 Fig_4/combined/f_0.1000<br> Fig_4/combined/f_0.0100<br> Fig_4/combined/f_0.0010<br> Fig_4/combined/f_0.0001 Fig_5/GAP-18_plus_SCs_f0.01 <br> Fig_5/GAP-18_plus_SCs_f0.001本数据集配套于Janine George、Geoffroy Hautier、Albert P. Bartók、Gábor Csányi与Volker L. Deringer发表的论文《高斯近似势（Gaussian Approximation Potential, GAP）模型中兼顾声子精度与高可迁移性》（Combining phonon accuracy with high transferability in Gaussian Approximation Potential models）。所有模型均由主参数文件`gp_iter6C.xml`以及关联文件`gp_iter6C.xml.sparseX.GAP_00001`定义，其中`GAP_0000`为该势函数唯一标识符的占位符（该标识符也会在XML文件头中给出），末尾的`1`表示仅包含一组描述符参数，此处为平滑原子位置重叠描述符（Smooth Overlap of Atomic Positions, SOAP）。本数据集的目录命名与论文中各势函数首次使用时对应的图表一致，具体目录如下： Fig_2/only_random/M_1000 Fig_2/only_random/M_3000 Fig_2/only_random/M_5000 Fig_2/only_random/M_7000 Fig_2/only_random/M_9000 Fig_2/only_individual/M_1000 Fig_2/only_individual/M_3000 Fig_2/only_individual/M_5000 Fig_2/only_individual/M_7000 Fig_2/only_individual/M_9000 Fig_2/combined/M_1000 Fig_2/combined/M_3000 Fig_2/combined/M_5000 Fig_2/combined/M_7000 Fig_2/combined/M_9000 Fig_4/only_individual/f_0.1000 Fig_4/only_individual/f_0.0100 Fig_4/only_individual/f_0.0010 Fig_4/only_individual/f_0.0001 Fig_4/combined/f_0.1000 Fig_4/combined/f_0.0100 Fig_4/combined/f_0.0010 Fig_4/combined/f_0.0001 Fig_5/GAP-18_plus_SCs_f0.01 Fig_5/GAP-18_plus_SCs_f0.001