| CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY |
|
|
|
|
Machine Learning to Instruct Single Crystal Growth by Flux Method |
| Tang-Shi Yao1,2†, Cen-Yao Tang1,2†, Meng Yang1,2†, Ke-Jia Zhu1,2, Da-Yu Yan1,2, Chang-Jiang Yi1,2, Zi-Li Feng1,2, He-Chang Lei4, Cheng-He Li4, Le Wang1,2, Lei Wang1**, You-Guo Shi1,2**, Yu-Jie Sun1,3,5**, Hong Ding1,3,5 |
1Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190
2University of Chinese Academy of Sciences, Beijing 100049
3CAS Centre for Excellence in Topological Quantum Computation, University of Chinese Academy of Sciences, Beijing 100049
4Department of Physics and Beijing Key Laboratory of Opto-electronic Functional Materials and Micro-nano Devices, Renmin University, Beijing 100872
5Songshan Lake Materials Laboratory, Dongguan 523808 |
|
| Cite this article: |
|
Tang-Shi Yao, Cen-Yao Tang, Meng Yang et al 2019 Chin. Phys. Lett. 36 068101 |
|
|
|
|
Abstract Growth of high-quality single crystals is of great significance for research of condensed matter physics. The exploration of suitable growing conditions for single crystals is expensive and time-consuming, especially for ternary compounds because of the lack of ternary phase diagram. Here we use machine learning (ML) trained on our experimental data to predict and instruct the growth. Four kinds of ML methods, including support vector machine (SVM), decision tree, random forest and gradient boosting decision tree, are adopted. The SVM method is relatively stable and works well, with an accuracy of 81% in predicting experimental results. By comparison, the accuracy of laboratory reaches 36%. The decision tree model is also used to reveal which features will take critical roles in growing processes.
|
|
Received: 30 April 2019
Published: 12 May 2019
|
|
| PACS: |
81.10.-h
|
(Methods of crystal growth; physics and chemistry of crystal growth, crystal morphology, and orientation)
|
| |
61.50.Ah
|
(Theory of crystal structure, crystal symmetry; calculations and modeling)
|
| |
81.05.-t
|
(Specific materials: fabrication, treatment, testing, and analysis)
|
| |
89.20.Ff
|
(Computer science and technology)
|
|
|
| Fund: Supported by the National Key Research and Development Program of China under Grant Nos 2016YFA0401000 and 2017YFA0302901, the National Basic Research Program of China under Grant No 2015CB921000, the National Natural Science Foundation of China under Grant Nos 11574371, 11774399 and 11774398, the Beijing Natural Science Foundation (Z180008), and the Strategic Priority Research Program of Chinese Academy of Sciences under Grant No XDB28000000. |
|
|
|
| [1] | Klitzing K v, Dorda G and Pepper M J P R L 1980 Phys. Rev. Lett. 45 494 | | [2] | Xu S Y, Belopolski I, Alidoust N, Neupane M, Bian G, Zhang C, Sankar R, Chang G, Yuan Z and Lee C C 2015 Science 349 613 | | [3] | Weng H, Fang C, Fang Z, Bernevig B A and Dai X 2015 Phys. Rev. X 5 011029 | | [4] | Chen X 2015 Sci. Chin. Mater. 58 675 | | [5] | Binnig G and Rohrer H 1983 Surf. Sci. 126 236 | | [6] | Damascelli A 2004 Phys. Scr. 2004 61 | | [7] | Fisher I, Shapiro M and Analytis J 2012 Philos. Mag. 92 2401 | | [8] | Canfield P C and Fisk Z 1992 Philos. Mag. B 65 1117 | | [9] | Raccuglia P, Elbert K C, Adler P D, Falk C, Wenny M B, Mollo A, Zeller M, Friedler S A, Schrier J and Norquist A J 2016 Nature 533 73 | | [10] | Nielsen J W and Dearborn E F 1958 J. Phys. Chem. Solids 5 202 | | [11] | Kohavi R 1996 Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (Portland, Oregon 1996) pp 202–207 | | [12] | Shevade S K, Keerthi S S, Bhattacharyya C and Murthy K R K 2000 IEEE Trans. Neural Netw. 11 1188 | | [13] | Shalev-Shwartz S, Singer Y, Srebro N and Cotter A 2011 Math. Programming 127 3 | | [14] | Cherkassky V and Ma Y 2004 Neural Networks 17 113 | | [15] | Joachims T 1998 Technical Report SFB 475 (Komplexit?tsreduktion in Multivariaten) | | [16] | Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K and Kuksa P 2011 J. Mach. Learn. Res. 12 2493 | | [17] | Ren S, He K, Girshick R and Sun J 2015 NIPS'15 Proceedings of the 28th International Conference on Neural Information Processing Systems (Montreal, Canada 7–12 December 2015) vol 1 p 91 | | [18] | Churchland P S, Sejnowski T J and Poggio T A 1992 The Computational Brain (Cambridge: MIT Press) p 544 | | [19] | Magerman D M, 1995 Proceedings of the 33rd annual meeting on Association for Computational Linguistics, Association for Computational Linguistics pp 276–283 | | [20] | Leslie C, Eskin E and Noble W S 2002 Pacific Symposium on Biocomputing 7 564 | | [21] | Zhang H, Berg A C and Maire M 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (New York, USA 17–22 June 2006) | | [22] | Brill E 1995 Comput. Linguistics 21 543 | | [23] | Hoo-Chang S, Roth H R, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D and Summers R M 2016 IEEE Trans. Med. Imaging 35 1285 | | [24] | Zhang Y and Ling C 2018 npj Comput. Mater. 4 25 | | [25] | Brockherde F, Vogt L, Li L, Tuckerman M E, Burke K and Müller K R 2017 Nat. Commun. 8 872 | | [26] | Snyder J C, Rupp M, Hansen K, Müller K R and Burke K 2012 Phys. Rev. Lett. 108 253002 | | [27] | Ren F, Ward L, Williams T, Laws K J, Wolverton C, Hattrick-Simpers J and Mehta A 2018 Sci. Adv. 4 eaaq1566 | | [28] | Pillong M, Marx C, Piechon P, Wicker J G, Cooper R I and Wagner T 2017 CrystEngComm 19 3737 | | [29] | Wicker J G and Cooper R I 2015 CrystEngComm 17 1927 | | [30] | Zhou Q, Tang P, Liu S, Pan J, Yan Q and Zhang S C 2018 Proc. Natl. Acad. Sci. USA 115 E6411 | | [31] | Curtarolo S, Hart G L, Nardelli M B, Mingo N and Sanvito S and Levy O 2013 Nat. Mater. 12 191 | | [32] | Butler K T, Davies D W, Cartwright H, Isayev O and Walsh A 2018 Nature 559 547 | | [33] | Natarajan A R and Van der Ven A 2018 npj Comput. Mater. 4 56 | | [34] | Tan P N, Steinbach M and Kumar V 2006 Introduction to Data Mining (New York: Pearson) vol 1 chap 2 p 41 | | [35] | Vapnik V N 1999 IEEE Trans. Neural Netw. 10 988 | | [36] | Quinlan J R 1986 IEEE Int. Workshop Mach. Learn. Signal Process. 1 81 | | [37] | Ho T K 1995 International Conference on Document Analysis and Recognition (Montreal, Quebec, Canada 14–16 August 1995) | | [38] | Friedman J H 2001 Ann. Stat. 29 1189 |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
