Preface
Sputtering yield which is a number of atoms removed from material's surface per an incident ion, is one of the most important parameters in various studies such as plasmamaterial interactions. Measurements of the yields have been performed by many authors for various combinations of substrates and incident ions, and the published values are available from literature. This web page provides sputtering yields at normal incident angle, which are predicted by a machine learning technique called Gaussian process regression (GPR) using those published yields as a learning dataset.
If you are new to use this page, please see Notes first to consider whether the model here is reasonable for your parameter (energy and sputtering yield) range or not.
Prediction
^{1}H  ^{2}He  
^{3}Li  ^{4}Be  ^{5}B  ^{6}C  ^{7}N  ^{8}O  ^{9}F  ^{10}Ne  
^{11}Na  ^{12}Mg  ^{13}Al  ^{14}Si  ^{15}P  ^{16}S  ^{17}Cl  ^{18}Ar  
^{19}K  ^{20}Ca  ^{21}Sc  ^{22}Ti  ^{23}V  ^{24}Cr  ^{25}Mn  ^{26}Fe  ^{27}Co  ^{28}Ni  ^{29}Cu  ^{30}Zn  ^{31}Ga  ^{32}Ge  ^{33}As  ^{34}Se  ^{35}Br  ^{36}Kr 
^{37}Rb  ^{38}Sr  ^{39}Y  ^{40}Zr  ^{41}Nb  ^{42}Mo  ^{43}Tc  ^{44}Ru  ^{45}Rh  ^{46}Pd  ^{47}Ag  ^{48}Cd  ^{49}In  ^{50}Sn  ^{51}Sb  ^{52}Te  ^{53}I  ^{54}Xe 
^{55}Cs  ^{56}Ba  ^{72}Hf  ^{73}Ta  ^{74}W  ^{75}Re  ^{76}Os  ^{77}Ir  ^{78}Pt  ^{79}Au  ^{80}Hg  ^{81}Tl  ^{82}Pb  ^{83}Bi  ^{84}Po  ^{85}At  ^{86}Rn  
^{87}Fr  ^{88}Ra  ^{104}Rf  ^{105}Db  ^{106}Sg  ^{107}Bh  ^{108}Hs  ^{109}Mt  ^{110}Ds  ^{111}Rg  ^{112}Cn  ^{113}Nh  ^{114}Fl  ^{115}Mc  ^{116}Lv  ^{117}Ts  ^{118}Og  
^{57}La  ^{58}Ce  ^{59}Pr  ^{60}Nd  ^{61}Pm  ^{62}Sm  ^{63}Eu  ^{64}Gd  ^{65}Tb  ^{66}Dy  ^{67}Ho  ^{68}Er  ^{69}Tm  ^{70}Yb  ^{71}Lu  
^{89}Ac  ^{90}Th  ^{91}Pa  ^{92}U  ^{93}Np  ^{94}Pu  ^{95}Am  ^{96}Cm  ^{97}Bk  ^{98}Cf  ^{99}Es  ^{100}Fm  ^{101}Md  ^{102}No  ^{103}Lr 
incident energy  :  eV  Compute 
sputtering yield  :  atoms/ion 
incident energy range  :   eV  Compute 
number of data points  : 
Notes

If you use the GPR prediction data above in your publication, please refer to this web site
https://wwwcamt.eng.osakau.ac.jp/hamaguchi/SY/
as well as the paper
Kino et al.: Phys. Plasmas 28, 013504 (2021). 
References of learning data
 S. Tachi and S. Okudaira: J. Vac. Sci. Technol. B 4(2), 459467 (1986).
 Y. Yamamura and H. Tawara: Atomic Data and Nuclear Data Tables 62, 149253 (1996).
 R. P. Doerner, D. G. Whyte, and D. M. Goebel: J. Appl. Phys. 93(9), 58165823 (2003).
 R. D. Kolasinski, J. E. Polk, D. Goebel, and L. K. Johnson: J. Vac. Sci. Technol. A 25(2), 236245 (2007).
 T. Ito and K. Karahashi: Si sputtering yields by halogen (F, Cl, Br) ions (private communications)
 S. Taira, T. Ito, and K. Karahashi: Mn sputtering yields by noble gas (Ne, Ar) ions (private communications)

Dataset property
Number of learning data points : 5687 Substrate element range : Be  U Incident ion element range : H  Pb 
Histograms of learning data
If the parameter range you would like to use is completely off from these learning dataset range, predicted sputtering yields may be much less accurate.

Prediction model development
The prediction model has been developed based on Gaussian process regression (GPR), which is a nonparametric regression method. The code of our model is written in python with GPy for GPR. The RBF kernel is used as a kernel function, and maximum likelihood estimation method and 10fold cross validation (CV) have been applied for tuning of hyperparameters.
Descriptors of the created model consist of atomic numbers and masses of substrate and incident ion elements, and an incident ion energy, additionally melting temperature of a substrate is also one of them.
The observedpredicted plot of the model for all learning data is shown here. The prediction ability in the range of the dataset is quite nice, such as that R^{2} coefficient is 0.98 and the root mean square error (RMSE) is 0.15 for test of CV with the best parameters.
Regression has been done for all dataset at once, so this model can predict sputtering yields even for combinations of no data by using similar conditions of descriptors.

Versions
Environment on development python : 3.8.3 GPy : 1.9.9 numpy : 1.19.5 prediction model : 0.0.3 (developed on ) web page : 0.2.2 (last updated on )  Recommended screen width: 1024px and more.
Contact us
Kazumasa Ikuse, Ph.D.
ikuse☆ppl.eng.osakau.ac.jp (Please replace "☆" in the email address with "@".)
We would appreciate it if you could share your sputtering yield data of singleelement substrates sputtered by singleelement incident ions, not used as training data in this model yet.
Your data will be added with proper references.