-
光电子能谱是一项在物质科学中被广泛应用的表征技术. 尤其是角分辨光电子能谱 (ARPES), 可以直接给出材料体系内电子的能量-动量色散关系和费米面结构, 是研究多体相互作用和关联量子材料的利器. 随着先进ARPES如时间分辨ARPES, Nano-ARPES等技术的不断发展, 以及同步辐射装置的更新换代, 将会产生越来越多的高通量实验数据. 因此, 探索准确、高效、同时能挖掘深层物理信息的数据处理方法变得愈发迫切. 由于机器学习天然具有的自动化处理复杂高维数据能力, 推动了包括ARPES在内的诸多领域的变革和技术创新. 本文综述了机器学习在光电子能谱中的应用, 包括对光谱数据进行降噪、进行电子结构分析、化学组成分析、以及结合理论计算获得的电子结构信息进行光谱预测. 进一步, 展望了更多机器学习算法在光电子能谱中的应用, 最终有望形成更加自动化的数据采集、预处理系统以及数据分析的工作流, 推动光电子能谱技术的发展, 从而推进量子材料和凝聚态物理前沿研究.
Photoelectron spectroscopy serves as a prevalent characterization technique in the field of materials science. Especially, angle-resolved photoelectron spectroscopy (ARPES) provides a direct method for determining the energy-momentum dispersion relationship and Fermi surface structure of electrons in a material system, therefore ARPES has become a potent tool for investigating many-body interactions and correlated quantum materials. With the emergence of technologies such as time-resolved ARPES and nano-ARPES, the field of photoelectron spectroscopy continues to advance. Meanwhile, the development of synchrotron radiation facilities has led to an increase of high-throughput and high-dimensional experimental data. This highlights the urgency for developing more efficient and accurate data processing methods, as well as extracting deeper physical information. In light of these developments, machine learning will play an increasingly significant role in various fields, including but not limited to ARPES. This paper reviews the applications of machine learning in photoelectron spectroscopy, mainly including the following three aspects. 1) Data Denoising Machine learning can be utilized for denoising photoelectron spectroscopy data. The denoising process via machine learning algorithms can be divided into two methods. Neither of the two methods need manual data annotation. The first method is to use noise generation algorithms to simulate experimental noise, so as to obtain effective low signal-to-noise ratio data pair to high signal-to-noise ratio data pair. And the second method is to extract noise and clean spectral data. 2) Electronic Structure and Chemical Composition Analysis Machine learning can be used for analyzing electronic structure and chemical composition. (Angle-resolved) photoelectron spectroscopy contains abundant information about material structure. Information such as energy band structure, self-energy, binding energy, and other condensed matter data can be rapidly acquired through machine learning schemes. 3) Prediction of Photoelectron Spectroscopy The electronic structure information obtained by combining first-principles calculation can also predict the photoelectron spectroscopy. The rapid acquisition of photoelectron spectroscopy data through machine learning algorithms also holds significance for material design. Photoelectron spectroscopy holds significant importance in the study of condensed matter physics. In the context of the development of synchrotron radiation, the construction of an automated data acquisition and analysis system can play a pivotal role in studying condensed matter physics. In addition, adding more physical constraints to the machine learning model will improve the interpretability and accuracy of the model. There exists a close relationship between photoelectron spectroscopy and first-principles calculations of electronic structure properties. The integration of these two through machine learning is anticipated to significantly contribute to the study of electronic structure properties. Furthermore, as machine learning algorithms continue to evolve, the application of more advanced machine learning algorithms in photoelectron spectroscopy research is expected. Building automated data acquisition and analysis systems, designing comprehensive workflows based on machine learning and first-principles methods, and integrating new machine learning techniques will help accelerate the progress of photoelectron spectroscopy experiments and facilitate the analysis of electronic structure properties and microscopic physical mechanisms, thereby advancing the frontier research in quantum materials and condensed matter physics. 
[1] Hoesch M, Greber T, Petrov V, Muntwiler M, Hengsberger M, Auwärter W, Osterwalder J 2002 J. Electron Spectrosc. Relat. Phenom. 124 263
Google Scholar
[2] Dil J H 2009 J. Phys.: Condes. Matter 21 403001
Google Scholar
[3] Yaji K, Harasawa A, Kuroda K, Toyohisa S, Nakayama M, Ishida Y, Fukushima A, Watanabe S, Chen C, Komori F, Shin S 2016 Rev. Sci. Instrum. 87 053111
Google Scholar
[4] Nordling C, Sokolowski E, Siegbahn K 1957 Phys. Rev. 105 1676
Google Scholar
[5] Damascelli A, Hussain Z, Shen Z X 2003 Rev. Mod. Phys. 75 473
Google Scholar
[6] Hashimoto M, He R H, Tanaka K, Testaud J P, Meevasana W, Moore R G, Lu D, Yao H, Yoshida Y, Eisaki H, Devereaux T P, Hussain Z, Shen Z X 2010 Nat. Phys. 6 414
Google Scholar
[7] Vishik I M, Hashimoto M, He R H, Lee W S, Schmitt F, Lu D, Moore R G, Zhang C, Meevasana W, Sasagawa T, Uchida S, Fujita K, Ishida S, Ishikado M, Yoshida Y, Eisaki H, Hussain Z, Devereaux T P, Shen Z X 2012 Proc. Natl. Acad. Sci. 109 18332
Google Scholar
[8] Ideta S, Johnston S, Yoshida T, Tanaka K, Mori M, Anzai H, Ino A, Arita M, Namatame H, Taniguchi M, Ishida S, Takashima K, Kojima K, Devereaux T, Uchida S, Fujimori A 2021 Phys. Rev. Lett. 127 217004
Google Scholar
[9] Gauvin-Ndiaye C, Setrakian M, Tremblay A M 2022 Phys. Rev. Lett. 128 087001
Google Scholar
[10] Maletz J, Zabolotnyy V B, Evtushinsky D V, Thirupathaiah S, Wolter A U B, Harnagea L, Yaresko A N, Vasiliev A N, Chareev D A, Böhmer A E, Hardy F, Wolf T, Meingast C, Rienks E D L, Büchner B, Borisenko S V 2014 Phys. Rev. B 89 220506
Google Scholar
[11] Yi M, Zhang Y, Shen Z X, Lu D 2017 npj Quantum Mater. 2 57
Google Scholar
[12] Cattelan M, Fox N A 2018 Nanomaterials 8 284
Google Scholar
[13] Sugawara K, Kusaka H, Kawakami T, Yanagizawa K, Honma A, Souma S, Nakayama K, Miyakawa M, Taniguchi T, Kitamura M, Horiba K, Kumigashira H, Takahashi T, Orimo S I, Toyoda M, Saito S, Kondo T, Sato T 2023 Nano Lett. 23 1673
Google Scholar
[14] Liu Z K, Zhou B, Zhang Y, Wang Z J, Weng H M, Prabhakaran D, Mo S K, Shen Z X, Fang Z, Dai X, Hussain Z, Chen Y L 2014 Science 343 864
Google Scholar
[15] Lv B, Qian T, Ding H 2019 Nat. Rev. Phys. 1 609
Google Scholar
[16] Zhong J, Yang M, Shi Z, Li Y, Mu D, Liu Y, Cheng N, Zhao W, Hao W, Wang J, Yang L, Zhuang J, Du Y 2023 Nat. Commun. 14 4964
Google Scholar
[17] Danzenbächer S, Vyalikh D V, Kummer K, Krellner C, Holder M, Höppner M, Kucherenko Y, Geibel C, Shi M, Patthey L, Molodtsov S L, Laubschat C 2011 Phys. Rev. Lett. 107 267601
Google Scholar
[18] Chang P Y, Erten O, Coleman P 2017 Nat. Phys. 13 794
Google Scholar
[19] Chen Q, Xu D, Niu X, Peng R, Xu H, Wen C, Liu X, Shu L, Tan S, Lai X, Zhang Y, Lee H, Strocov V, Bisti F, Dudin P, Zhu J X, Yuan H, Kirchner S, Feng D 2018 Phys. Rev. Lett. 120 066403
Google Scholar
[20] Zhang Y, Luo X, Feng W, Tan S, Hao Q, Zhang Q, Yuan D, Wang B, Liu Y, Liu Q, Wang X, Luo L, Zhu X, Chen Q, Lai X 2022 Phys. Rev. B 106 045133
Google Scholar
[21] Sobota J A, He Y, Shen Z X 2021 Rev. Mod. Phys. 93 025006
Google Scholar
[22] Xu S Y, Alidoust N, Belopolski I, Yuan Z, Bian G, Chang T R, Zheng H, Strocov V N, Sanchez D S, Chang G, Zhang C, Mou D, Wu Y, Huang L, Lee C C, Huang S M, Wang B, Bansil A, Jeng H T, Neupert T, Kaminski A, Lin H, Jia S, Zahid Hasan M 2015 Nat. Phys. 11 748
Google Scholar
[23] Liu Z K, Yang L X, Sun Y, Zhang T, Peng H, Yang H F, Chen C, Zhang Y, Guo Y, Prabhakaran D, Schmidt M, Hussain Z, Mo S K, Felser C, Yan B, Chen Y L 2016 Nat. Mater. 15 27
Google Scholar
[24] Belopolski I, Xu S Y, Sanchez D S, Chang G, Guo C, Neupane M, Zheng H, Lee C C, Huang S M, Bian G, Alidoust N, Chang T R, Wang B, Zhang X, Bansil A, Jeng H T, Lin H, Jia S, Hasan M Z 2016 Phys. Rev. Lett. 116 066802
Google Scholar
[25] Tanaka H, Telegin A V, Sukhorukov Y P, Golyashov V A, Tereshchenko O E, Lavrov A N, Matsuda T, Matsunaga R, Akashi R, Lippmaa M, Arai Y, Ideta S, Tanaka K, Kondo T, Kuroda K 2023 Phys. Rev. Lett. 130 186402
Google Scholar
[26] Tang S, Zhang C, Wong D, Pedramrazi Z, Tsai H Z, Jia C, Moritz B, Claassen M, Ryu H, Kahn S, Jiang J, Yan H, Hashimoto M, Lu D, Moore R G, Hwang C C, Hwang C, Hussain Z, Chen Y, Ugeda M M, Liu Z, Xie X, Devereaux T P, Crommie M F, Mo S K, Shen Z X 2017 Nat. Phys. 13 683
Google Scholar
[27] Schmitt F, Kirchmann P S, Bovensiepen U, Moore R G, Rettig L, Krenz M, Chu J H, Ru N, Perfetti L, Lu D H, Wolf M, Fisher I R, Shen Z X 2008 Science 321 1649
Google Scholar
[28] Rohwer T, Hellmann S, Wiesenmayer M, Sohrt C, Stange A, Slomski B, Carr A, Liu Y, Avila L M, Kalläne M, Mathias S, Kipp L, Rossnagel K, Bauer M 2011 Nature 471 490
Google Scholar
[29] Wang Y, Hsieh D, Sie E, Steinberg H, Gardner D, Lee Y, Jarillo-Herrero P, Gedik N 2012 Phys. Rev. Lett. 109 127401
Google Scholar
[30] Ossiander M, Riemensberger J, Neppl S, Mittermair M, Schäffer M, Duensing A, Wagner M S, Heider R, Wurzer M, Gerl M, Schnitzenbaumer M, Barth J V, Libisch F, Lemell C, Burgdörfer J, Feulner P, Kienberger R 2018 Nature 561 374
Google Scholar
[31] Fan H 1945 Phys. Rev. 68 43
Google Scholar
[32] Berglund C N, Spicer W E 1964 Phys. Rev. 136 A1030
Google Scholar
[33] Damascelli A 2004 Phys. Scr. 2004 61
Google Scholar
[34] Strocov V 2003 J. Electron Spectrosc. Relat. Phenom. 130 65
Google Scholar
[35] Seah M P, Dench W 1979 Surf. Interface Anal. 1 2
Google Scholar
[36] Strocov V, Starnberg H, Nilsson P, Brauer H, Holleboom L 1997 Phys. Rev. Lett. 79 467
Google Scholar
[37] Strocov V N, Shi M, Kobayashi M, Monney C, Wang X, Krempasky J, Schmitt T, Patthey L, Berger H, Blaha P 2012 Phys. Rev. Lett. 109 086401
Google Scholar
[38] Leemann S, Liu S, Hexemer A, Marcus M, Melton C, Nishimura H, Sun C 2019 Phys. Rev. Lett. 123 194801
Google Scholar
[39] Goodman J, King M, Dolier E, Wilson R, Gray R, McKenna P 2023 High Power Laser Sci. Eng. 11 e34
Google Scholar
[40] Pan D, Fan J, Nie Z, Sun Z, Zhang J, Tong Y, He B, Song C, Kohmura Y, Yabashi M, Ishikawa T, Shen Y, Jiang H 2022 IUCrJ 9 223
Google Scholar
[41] Zhou Z, Li C, Bi X, Zhang C, Huang Y, Zhuang J, Hua W, Dong Z, Zhao L, Zhang Y, Dong Y 2023 npj Comput. Mater. 9 58
Google Scholar
[42] Asahara A, Morita H, Ono K, Mitsumata C, Yano M, Shoji T 2019 Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence 33 9410
Google Scholar
[43] Chang M C, Wei Y, Chen W R, Do C 2020 MRS Commun. 10 11
Google Scholar
[44] Belič I, Poniku B, Jenko M 2012 Surf. Interface Anal. 44 1141
Google Scholar
[45] Yoon T, Kim S W, Byun H, Kim Y, Carter C D, Do H 2023 Combust. Flame 248 112583
Google Scholar
[46] Planckaert N, Demeulemeester J, Laenens B, Smeets D, Meersschaut J, L’abbé C, Temst K, Van-tomme A 2010 J. Synchrot. Radiat. 17 86
Google Scholar
[47] Martini A, Guda S, Guda A, Smolentsev G, Algasov A, Usoltsev O, Soldatov M, Bugaev A, Rusalev Y, Lamberti C, Soldatov A 2020 Comput. Phys. Commun. 250 107064
Google Scholar
[48] Roch L M, Saikin S K, Hase F, Friederich P, Goldsmith R H, León S, Aspuru-Guzik A 2020 ACS Nano 14 6589
Google Scholar
[49] Scarborough N M, Godaliyadda G M D P, Ye D H, Kissick D J, Zhang S, Newman J A, Sheedlo M J, Chowdhury A U, Fischetti R F, Das C, Buzzard G T, Bouman C A, Simpson G J 2017 J. Synchrot. Radiat. 24 188
Google Scholar
[50] Ke T W, Brewster A S, Yu S X, Ushizima D, Yang C, Sauter N K 2018 J. Synchrot. Radiat. 25 655
Google Scholar
[51] Sullivan B, Archibald R, Azadmanesh J, Vandavasi V G, Langan P S, Coates L, Lynch V, Langan P 2019 J. Appl. Crystallogr. 52 854
Google Scholar
[52] Lolla S, Liang H, Kusne A G, Takeuchi I, Ratcliff W 2022 J. Appl. Crystallogr. 55 882
Google Scholar
[53] Boulle A, Debelle A 2023 Mach. Learn.: Sci. Technol. 4 015002
Google Scholar
[54] Zhao C, Yu W, Li L 2023 Mater. Des. 228 111828
Google Scholar
[55] Kopp R, Joseph J, Ni X, Roy N, Wardle B L 2022 Adv. Mater. 34 2107817
Google Scholar
[56] Hendriksen A A, Bührer M, Leone L, Merlini M, Vigano N, Pelt D M, Marone F, Di Michiel M, Batenburg K J 2021 Sci Rep 11 11895
Google Scholar
[57] Huang D, Liu J, Qian T, Yang Y F 2023 Sci. China Phys. Mech. Astron. 66 267011
Google Scholar
[58] Pelzer K, Schwarz N, Harder R 2021 J. Appl. Crystallogr. 54 523
Google Scholar
[59] Thakur R S, Chatterjee S, Yadav R N, Gupta L 2021 IEEE Access 9 93338
Google Scholar
[60] Kim Y, Oh D, Huh S, Song D, Jeong S, Kwon J, Kim M, Kim D, Ryu H, Jung J, Kyung W, Sohn B, Lee S, Hyun J, Lee Y, Kim Y, Kim C 2021 Rev. Sci. Instrum. 92 073901
Google Scholar
[61] Restrepo F, Zhao J, Chatterjee U 2022 Rev. Sci. Instrum. 93 065106
Google Scholar
[62] Liu J, Huang D, Yang Y F, Qian T 2023 Phys. Rev. B 107 165106
Google Scholar
[63] Sun E 2022 IEEE MIT Undergraduate Research Technology Conference (URTC) Cambridge, MA, USA, 30 September 2022–02 October 2022, p1
[64] Iwasawa H, Ueno T, Masui T, Tajima S 2022 npj Quantum Mater. 7 24
Google Scholar
[65] Melton C N, Noack M M, Ohta T, Beechem T E, Robinson J, Zhang X, Bostwick A, Jozwiak C, Koch R J, Zwart P H, Hexemer A, Rotenberg E 2020 Mach. Learn.: Sci. Technol. 1 045015
Google Scholar
[66] Ekahana S A, Winata G I, Soh Y, Tamai A, Milan R, Aeppli G, Shi M 2023 Mach. Learn.: Sci. Technol. 4 035021
Google Scholar
[67] Park S H, Park H, Lee H, Kim H S 2021 J. Korean Phys. Soc. 79 1199
Google Scholar
[68] Pielsticker L, Nicholls R L, DeBeer S, Greiner M 2023 Anal. Chim. Acta 1271 341433
Google Scholar
[69] Xian R P, Stimper V, Zacharias M, Dendzik M, Dong S, Beaulieu S, Schölkopf B, Wolf M, Rettig L, Carbogno C, Bauer S, Ernstorfer R 2023 Nat. Comput. Sci. 3 101
Google Scholar
[70] Norman M, Eschrig M, Kaminski A, Campuzano J 2001 Phys. Rev. B 64 184508
Google Scholar
[71] Zhang H, Pincelli T, Jozwiak C, Kondo T, Ernstorfer R, Sato T, Zhou S 2022 Nat. Rev. Method. Prim. 2 54
Google Scholar
[72] Iwasawa H, Yoshida Y, Hase I, Shimada K, Namatame H, Taniguchi M, Aiura Y 2013 Sci. Rep. 3 1930
Google Scholar
[73] Yamaji Y, Yoshida T, Fujimori A, Imada M 2021 Phys. Rev. Res. 3 043099
Google Scholar
[74] Hohenberg P, Kohn W 1964 Phys. Rev. 13 6
Google Scholar
[75] Kohn W, Sham L J 1965 Phys. Rev. 140 A1133
Google Scholar
[76] Perdew J P, Burke K, Ernzerhof M 1996 Phys. Rev. Lett. 77 3865
Google Scholar
[77] Heyd J, Scuseria G E, Ernzerhof M 2003 J. Chem. Phys. 118 8207
Google Scholar
[78] Zhu X, Louie S G 1991 Phys. Rev. B 43 14142
Google Scholar
[79] Zanolli Z, Fuchs F, Furthmüller J, von Barth U, Bechstedt F 2007 Phys. Rev. B 75 245121
Google Scholar
[80] Aryasetiawan F, Gunnarsson O 1998 Rep. Prog. Phys. 61 237
Google Scholar
[81] Reining L 2018 Wiley Interdiscip. Rev.-Comput. Mol. Sci. 8 e1344
Google Scholar
[82] Golze D, Dvorak M, Rinke P 2019 Front. Chem. 7 377
Google Scholar
[83] Anisimov V I, Zaanen J, Andersen O K 1991 Phys. Rev. B 44 943
Google Scholar
[84] Dudarev S L, Botton G A, Savrasov S Y, Humphreys C, Sutton A P 1998 Phys. Rev. B 57 1505
Google Scholar
[85] Yu M, Yang S, Wu C, Marom N 2020 npj Comput. Mater. 6 180
Google Scholar
[86] Harun K, Salleh N A, Deghfel B, Yaakob M K, Mohamad A A 2020 Results Phys. 16 102829
Google Scholar
[87] Cococcioni M, De Gironcoli S 2005 Phys. Rev. B 71 035105
Google Scholar
[88] Kulik H J, Cococcioni M, Scherlis D A, Marzari N 2006 Phys. Rev. Lett. 97 103001
Google Scholar
[89] Mosey N J, Carter E A 2007 Phys. Rev. B 76 155123
Google Scholar
[90] Mosey N J, Liao P, Carter E A 2008 J. Chem. Phys. 129 014103
Google Scholar
[91] Aryasetiawan F, Karlsson K, Jepsen O, Schönberger U 2006 Phys. Rev. B 74 125106
Google Scholar
[92] Miyake T, Aryasetiawan F 2008 Phys. Rev. B 77 085122
Google Scholar
[93] Şaşıoğlu E, Friedrich C, Blügel S 2011 Phys. Rev. B 83 121101
Google Scholar
[94] Setvin M, Franchini C, Hao X, Schmid M, Janotti A, Kaltak M, Van de Walle C G, Kresse G, Diebold U 2014 Phys. Rev. Lett. 113 086402
Google Scholar
[95] Falletta S, Pasquarello A 2022 npj Comput. Mater. 8 263
Google Scholar
[96] Tavadze P, Boucher R, Avendaño-Franco G, Kocan K X, Singh S, Dovale-Farelo V, Ibarra-Hernández W, Johnson M B, Mebane D S, Romero A H 2021 npj Comput. Mater. 7 182
Google Scholar
[97] Golze D, Hirvensalo M, Hernández-León P, Aarva A, Etula J, Susi T, Rinke P, Laurila T, Caro M A 2022 Chem. Mat. 34 6240
Google Scholar
[98] Sun Q, Xiang Y, Liu Y, Xu L, Leng T, Ye Y, Fortunelli A, Goddard Ⅲ W A, Cheng T 2022 J. Phys. Chem. Lett. 13 8047
Google Scholar
[99] Yang S, Schröter N B M, Strocov V N, Schuwalow S, Rajpalk M, Ohtani K, Krogstrup P, Winkler G W, Gukelberger J, Gresch D, Aeppli G, Lutchyn R M, Marom N 2022 Adv. Quantum Technol. 5 2100033
Google Scholar
[100] Jardine M J A, Dardzinski D, Yu M, Purkayastha A, Chen A H, Chang Y H, Engel A, Strocov V N, Hocevar M, Palmstrom C, Frolov S M, Marom N 2023 ACS Appl. Mater. Interfaces 15 16288
Google Scholar
[101] Bubert H, Hillig H 2000 Microchim. Acta 133 95
Google Scholar
[102] Kim B, Kim W S 2007 Microelectron. Eng. 84 584
Google Scholar
[103] Kim B, Kim G T, Lee H J 2008 Mater. Manuf. Process. 23 528
Google Scholar
[104] Kim B, Kim J, Choi S 2009 Expert Syst. Appl. 36 11347
Google Scholar
[105] Englert T, Gruber F, Stiedl J, Green S, Jacob T, Rebner K, Grählert W 2021 Sensors 21 5595
Google Scholar
[106] Drera G, Kropf C M, Sangaletti L 2020 Mach. Learn.: Sci. Technol. 1 015008
Google Scholar
[107] Baker N, Alexander F, Bremer T, Hagberg A, Kevrekidis Y, Najm H, Parashar M, Patra A, Sethian J, Wild S, Willcox K, Lee S 2019 Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence (Washington DC, United States: USDOE Office of Science (SC)) 1478744
[108] Choudhary K, DeCost B, Chen C, Jain A, Tavazza F, Cohn R, Park C W, Choudhary A, Agrawal A, Billinge S J L, Holm E, Ong S P, Wolverton C 2022 npj Comput. Mater. 8 59
Google Scholar
[109] Cranmer M, Sanchez-Gonzalez A, Battaglia P, Xu R, Cranmer K, Spergel D, Ho S 2020 NIPS ’20: Proceedings of the 34th International Conference on Neural Information Processing Systems (Vancouver, BC, Canada: Curran Associates Inc.) p17429
[110] Cranmer M, Greydanus S, Hoyer S, Battaglia P, Spergel D, Ho S 2020 arXiv: 2003.04630 [physics.comp-ph]
[111] Samarakoon A M, Laurell P, Balz C, Banerjee A, Lampen-Kelley P, Mandrus D, Nagler S E, Okamoto S, Tennant D A 2022 Phys. Rev. Res. 4 L022061
Google Scholar
[112] Schütt K T, Sauceda H E, Kindermans P J, Tkatchenko A, Müller K R 2018 J. Chem. Phys. 148 241722
Google Scholar
[113] Sobral J A, Obernauer S, Turkel S, Pasupathy A N, Scheurer M S 2023 Nat. Commun. 14 5012
Google Scholar
[114] Chen Z, Andrejevic N, Drucker N C, Nguyen T, Xian R P, Smidt T, Wang Y, Ernstorfer R, Tennant D A, Chan M, Li M 2021 Chem. Phys. Rev. 2 031301
Google Scholar
[115] Doucet M, Samarakoon A M, Do C, Heller W T, Archibald R, Tennant D A, Proffen T, Granroth G E 2020 Mach. Learn.: Sci. Technol. 2 023001
Google Scholar
[116] Chitturi S R, Ratner D, Walroth R C, Thampy V, Reed E J, Dunne M, Tassone C J, Stone K H 2021 J. Appl. Crystallogr. 54 1799
Google Scholar
[117] Matsumura T, Nagamura N, Akaho S, Nagata K, Ando Y 2019 Sci. Technol. Adv. Mater. 20 733
Google Scholar
[118] Xi B, Tse K F, Kok T F, Chan H M, Chan M K, Chan H Y, Clinton Wong K Y, Robin Yuen S H, Zhu J 2022 J. Phys. Chem. C 126 12264
Google Scholar
[119] Bergstra J, Bengio Y 2012 J. Mach. Learn. Res. 13 281
Google Scholar
[120] Bergstra J, Bardenet R, Bengio Y, Kégl B 2011 Proceedings of the 24th International Conference on Neural Information Processing Systems (Vol. 24 of NIPS’11) (Granada: Curran Associates, Inc.) p2546
[121] Gardner J R, Kusner M J, Xu Z E, Weinberger K Q, Cunningham J P 2014 Proceedings of the 31st International Conference on International Conference on Machine Learning (Vol. 32 of ICML’14) (Beijing, China: JMLR.org) p II-937
[122] Bergstra J, Yamins D, Cox D 2013 Procedings of the 30 th International Conference on Machine Learning (Vol. 28 of ICML’13) (Atlanta, GA, USA: JMLR.org) p I-115
[123] Akiba T, Sano S, Yanase T, Ohta T, Koyama M 2019 Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Vol. 18 of KDD ’19) (Anchorage, AK, USA: ACM) p2623
[124] Kvasnicka V, Sklenak S, Pospichal J 1992 J. Chem. Inf. Comput. Sci. 32 742
Google Scholar
[125] Simine L, Allen T C, Rossky P J 2020 Proc. Natl. Acad. Sci. 117 13945
Google Scholar
[126] Urbina F, Batra K, Luebke K J, White J D, Matsiev D, Olson L L, Malerich J P, Hupcey M A, Madrid P B, Ekins S 2021 Anal. Chem. 93 16076
Google Scholar
[127] Wu X, Zhao Z, Tian R, Niu Y, Gao S, Liu H 2021 Spectroc. Acta Pt. A: Molec. Biomolec. Spectr. 244 118841
Google Scholar
[128] Scarselli F, Gori M, Tsoi A C, Hagenbuchner M, Monfardini G 2009 IEEE Trans. Neural Netw. 20 61
Google Scholar
[129] Coley C W, Jin W, Rogers L, Jamison T F, Jaakkola T S, Green W H, Barzilay R, Jensen K F 2019 Chem. Sci. 10 370
Google Scholar
[130] Stärk H, Beaini D, Corso G, Tossou P, Dallago C, Günnemann S, Lió P 2022 Proceedings of the 39th International Conference on Machine Learning (Vol. 162 of Proceedings of Machine Learning Research) (Baltimore, MD, USA: PMLR) p20479
[131] Xie T, Grossman J C 2018 Phys. Rev. Lett. 120 145301
Google Scholar
[132] Gao W, Mahajan S P, Sulam J, Gray J J 2020 Patterns 1 100142
Google Scholar
[133] Choudhary K, DeCost B 2021 npj Comput. Mater. 7 185
Google Scholar
[134] Bang K, Yeo B C, Kim D, Han S S, Lee H M 2021 Sci. Rep. 11 11604
Google Scholar
[135] Kong S, Ricci F, Guevarra D, Neaton J B, Gomes C P, Gregoire J M 2022 Nat. Commun. 13 949
Google Scholar
[136] Fung V, Ganesh P, Sumpter B G 2022 Chem. Mat. 34 4848
Google Scholar
[137] Kaundinya P R, Choudhary K, Kalidindi S R 2022 JOM 74 1395
Google Scholar
[138] Masood H, Sirojan T, Toe C Y, Kumar P V, Haghshenas Y, Sit P H, Amal R, Sethu V, Teoh W Y 2023 Cell Rep. Phys. Sci. 4 101555
Google Scholar
[139] Lee J, Asahi R 2021 Comput. Mater. Sci. 190 110314
Google Scholar
[140] Li B, Rangarajan S 2022 Comput. Chem. Eng. 157 107599
Google Scholar
[141] Tian S I P, Ren Z, Venkataraj S, Cheng Y, Bash D, Oviedo F, Senthilnath J, Chellappan V, Lim Y F, Aberle A G, MacLeod B P, Parlane F G L, Berlinguette C P, Li Q, Buonassisi T, Liu Z 2023 Digit. Discov. 2 1334
Google Scholar
[142] Zuo H, Zhang G, Pedrycz W, Behbood V, Lu J 2016 IEEE Trans. Fuzzy Syst. 25 1795
Google Scholar
[143] Wang L, Zhang C, Bai R, Li J, Duan H 2020 Chem. Commun. 56 9368
Google Scholar
[144] Yamada H, Liu C, Wu S, Koyama Y, Ju S, Shiomi J, Morikawa J, Yoshida R 2019 ACS Central Sci. 5 1717
Google Scholar
[145] Pan S J, Yang Q 2009 IEEE Trans. Knowl. Data Eng. 22 1345
Google Scholar
[146] Xu P, Ji X, Li M, Lu W 2023 npj Comput. Mater. 9 42
Google Scholar
-
图 1 (a)近年来光电子能谱相关文章的发文数量; (b)光电子能谱常见应用领域(来源: web of science——以光电子能谱相关关键词搜索获得的结果); (c)光电子能谱常见的研究体系
Fig. 1. (a) The number of papers related to photoelectron spectroscopy in recent years; (b) common application fields of photoelectron spectroscopy (Source: web of science—Results obtained by searching for keywords related to photoelectron spectroscopy); (c) common research systems of photoelectron spectroscopy.
图 2 (a)光电子激发和采集示意图; (b)光电子强度和电子态密度的关系; (c)材料内部光电子平均自由程与光子能量的关系. (b)引用自参考文献[14], 版权属于 Springer Nature; (c)引用自参考文献[35], 版权属于 John Wiley and Sons
Fig. 2. (a) Photoelectron excitation and collection schematics; (b) relationship between photoelectron intensity and electron density of states; (c) relationship between the average free path of photoelectrons inside the material and photon energy. Panel (b) reprinted with permission from Ref. [14], copyright 2019 by the Springer Nature; panel (c) reprinted with permission from Ref. [35], copyright 1979 by the John Wiley and Sons.
图 3 机器学习在光电子能谱中的作用. 机器学习的应用主要分为四个方面, 分别是对光电子能谱数据进行降噪; 加速元素分析; 提取光电子能谱中的物理信息(如电子结构信息); 以及通过结合理论计算的结果预测材料的光电子能谱
Fig. 3. Role of machine learning in photoelectron spectroscopy. The application of machine learning is mainly divided into four aspects: noise reduction of photoelectron spectroscopy data; accelerated elemental analysis; extraction of physical information in the photoelectron spectroscopy (such as the electronic structure information); and the photoelectron spectroscopy of the material is predicted by combining the results of theoretical calculations.
图 4 光电子能谱数据降噪中的机器学习方法. 方法一: 生成噪声数据模拟实验噪声, 从而进行降噪网络的训练[60,61]; 方法二: 通过不同的网络分别提取噪声和干净的光谱数据, 然后将两者组合形成生成数据. 因此, 两种方法的损失函数都是通过评估生成数据与原始数据的相似性[57,62]
Fig. 4. Machine learning methods in noise reduction of photoelectron spectroscopy data. Method 1: noise data is generated to simulate the noise, so as to train the noise reduction network[60,61]; method 2: noise and clean spectral data are extracted by different networks, and then combined to form the generated data. Therefore, the loss function of both methods is to evaluate the similarity between the generated data and the original data[57,62].
图 5 聚类数为8时k-means的结果 (a)不同簇数时, 每个簇的空间分布; (b)对每个簇中的簇成员进行平均得到的平均EDC. 引用自参考文献[64], 版权属于 Springer Nature
Fig. 5. Results of k-means when the number of clusters is 8: (a) Spatial distribution of each of clusters for different number of clusters; (b) mean-EDCs obtained by averaging the cluster members in each cluster. Reprinted with permission from Ref. [64], copyright 2022 by the Springer Nature.
图 6 (a)马尔可夫随机场进行能带重建过程: 实验获得的ARPES数据经过预处理和第一性原理计算的初始值输入到马尔可夫随机场中, 得到的结果经过后处理便能形成按能带指数排列的光电发射色散面, 即能带结构; (b)重建的14层价带; (c)重建出的能带色散(红色线条)与在光电子能带映射数据的叠加. 引用自参考文献[69], 版权属于 Springer Nature
Fig. 6. (a) Band reconstruction process with Markov random field: The ARPES data obtained from the experiment are preprocessed, and the initial values of the first-principles calculation are input into the Markov random field. The obtained results are post-processed to form a photoelectric emission dispersion surface arranged exponentially according to the energy band, that is, the band structure; (b) the reconstructed 14-layer valence band; (c) the superposition of the reconstructed band dispersion (red line) and the data mapped in the photoelectron energy band. Reprinted with permission from Ref. [69], copyright 2022 by the Springer Nature.
图 7 自能提取的机器学习的流程, 用于从实验观测的光电子谱函数$ A({\boldsymbol{k}}, \omega) $中提取正常自能和反常自能. 引用自参考文献[73], 版权属于美国物理学会
Fig. 7. Flow chart of machine-learning procedure. It is used to extract normal self-energy $ {{\varSigma}}({\boldsymbol{k}}, \omega)^\text{nor} $ and anomalous self-energy $ {{\varSigma}}({\boldsymbol{k}}, \omega)^\text{ano} $ from the experimentally observed spectral function $ A({\boldsymbol{k}}, \omega) $. Reprinted with permission from Ref. [73], copyright 2021 by the American Physical Society.
图 8 使用原子位置平滑重叠和核岭回归预测结合能和XPS (a)使用SOAP多体描述符处理CHO材料数据库获得的基于聚类的多维缩放图; (b)—(d) a-COx的C 1s谱. 其中浅灰色的C原子贡献了光谱中的浅灰色区域, 而深灰色的C原子贡献了光谱中的深灰色区域. 引用自参考文献[97], 版权属于美国化学会
Fig. 8. Smooth overlap of atomic positions and kernel ridge regression are used to predict the binding energy and XPS: (a) Using SOAP multi-body descriptor to process the cluster-based multidimensional scaling map obtained from CHO material database; (b)–(d) the C 1s spectra of a-COx, the light gray C atoms contribute to the light gray region in the spectrum, while the dark gray C atoms contribute to the dark gray region in the spectrum. Reprinted with permission from Ref. [97], copyright 2022 by the American Chemical Society.
图 9 (a) $ {\rm{InAs}}(001) \ \beta 2(2 \times 4) $ 俯视图和侧视图; (b), (c), (d) 分别为 $ {\rm{InAs}}(001) \ \beta 2(2 \times 4) $能带结构的xy-unfolding, z-unfolding, bulk unfolding; (e) $ {\rm{InAs}}(001) \ \beta 2(2 \times 4) $ 表面的布里渊区. 引用自参考文献[99], 版权属于 John Wiley and Sons
Fig. 9. (a) $ {\rm{InAs}}(001) \ \beta 2(2 \times 4) $ top view and side view; (b), (c), (d) xy-unfolding, z-unfolding, bulk unfolding of $ {\rm{InAs}}(001) \ \beta 2 $$ (2 \times 4) $ band structure, respectively; (e) the Brillouin zone of $ {\rm{InAs}}(001) \ \beta 2(2 \times 4) $ surface. Reprinted with permission from Ref. [99], copyright 2022 by the John Wiley and Sons.
图 10 现有的以及未来可用于角分辨光电子能谱的机器学习方法
Fig. 10. Existing and future machine learning methods for angle-resolved photoelectron spectroscopy.
-
[1] Hoesch M, Greber T, Petrov V, Muntwiler M, Hengsberger M, Auwärter W, Osterwalder J 2002 J. Electron Spectrosc. Relat. Phenom. 124 263
Google Scholar
[2] Dil J H 2009 J. Phys.: Condes. Matter 21 403001
Google Scholar
[3] Yaji K, Harasawa A, Kuroda K, Toyohisa S, Nakayama M, Ishida Y, Fukushima A, Watanabe S, Chen C, Komori F, Shin S 2016 Rev. Sci. Instrum. 87 053111
Google Scholar
[4] Nordling C, Sokolowski E, Siegbahn K 1957 Phys. Rev. 105 1676
Google Scholar
[5] Damascelli A, Hussain Z, Shen Z X 2003 Rev. Mod. Phys. 75 473
Google Scholar
[6] Hashimoto M, He R H, Tanaka K, Testaud J P, Meevasana W, Moore R G, Lu D, Yao H, Yoshida Y, Eisaki H, Devereaux T P, Hussain Z, Shen Z X 2010 Nat. Phys. 6 414
Google Scholar
[7] Vishik I M, Hashimoto M, He R H, Lee W S, Schmitt F, Lu D, Moore R G, Zhang C, Meevasana W, Sasagawa T, Uchida S, Fujita K, Ishida S, Ishikado M, Yoshida Y, Eisaki H, Hussain Z, Devereaux T P, Shen Z X 2012 Proc. Natl. Acad. Sci. 109 18332
Google Scholar
[8] Ideta S, Johnston S, Yoshida T, Tanaka K, Mori M, Anzai H, Ino A, Arita M, Namatame H, Taniguchi M, Ishida S, Takashima K, Kojima K, Devereaux T, Uchida S, Fujimori A 2021 Phys. Rev. Lett. 127 217004
Google Scholar
[9] Gauvin-Ndiaye C, Setrakian M, Tremblay A M 2022 Phys. Rev. Lett. 128 087001
Google Scholar
[10] Maletz J, Zabolotnyy V B, Evtushinsky D V, Thirupathaiah S, Wolter A U B, Harnagea L, Yaresko A N, Vasiliev A N, Chareev D A, Böhmer A E, Hardy F, Wolf T, Meingast C, Rienks E D L, Büchner B, Borisenko S V 2014 Phys. Rev. B 89 220506
Google Scholar
[11] Yi M, Zhang Y, Shen Z X, Lu D 2017 npj Quantum Mater. 2 57
Google Scholar
[12] Cattelan M, Fox N A 2018 Nanomaterials 8 284
Google Scholar
[13] Sugawara K, Kusaka H, Kawakami T, Yanagizawa K, Honma A, Souma S, Nakayama K, Miyakawa M, Taniguchi T, Kitamura M, Horiba K, Kumigashira H, Takahashi T, Orimo S I, Toyoda M, Saito S, Kondo T, Sato T 2023 Nano Lett. 23 1673
Google Scholar
[14] Liu Z K, Zhou B, Zhang Y, Wang Z J, Weng H M, Prabhakaran D, Mo S K, Shen Z X, Fang Z, Dai X, Hussain Z, Chen Y L 2014 Science 343 864
Google Scholar
[15] Lv B, Qian T, Ding H 2019 Nat. Rev. Phys. 1 609
Google Scholar
[16] Zhong J, Yang M, Shi Z, Li Y, Mu D, Liu Y, Cheng N, Zhao W, Hao W, Wang J, Yang L, Zhuang J, Du Y 2023 Nat. Commun. 14 4964
Google Scholar
[17] Danzenbächer S, Vyalikh D V, Kummer K, Krellner C, Holder M, Höppner M, Kucherenko Y, Geibel C, Shi M, Patthey L, Molodtsov S L, Laubschat C 2011 Phys. Rev. Lett. 107 267601
Google Scholar
[18] Chang P Y, Erten O, Coleman P 2017 Nat. Phys. 13 794
Google Scholar
[19] Chen Q, Xu D, Niu X, Peng R, Xu H, Wen C, Liu X, Shu L, Tan S, Lai X, Zhang Y, Lee H, Strocov V, Bisti F, Dudin P, Zhu J X, Yuan H, Kirchner S, Feng D 2018 Phys. Rev. Lett. 120 066403
Google Scholar
[20] Zhang Y, Luo X, Feng W, Tan S, Hao Q, Zhang Q, Yuan D, Wang B, Liu Y, Liu Q, Wang X, Luo L, Zhu X, Chen Q, Lai X 2022 Phys. Rev. B 106 045133
Google Scholar
[21] Sobota J A, He Y, Shen Z X 2021 Rev. Mod. Phys. 93 025006
Google Scholar
[22] Xu S Y, Alidoust N, Belopolski I, Yuan Z, Bian G, Chang T R, Zheng H, Strocov V N, Sanchez D S, Chang G, Zhang C, Mou D, Wu Y, Huang L, Lee C C, Huang S M, Wang B, Bansil A, Jeng H T, Neupert T, Kaminski A, Lin H, Jia S, Zahid Hasan M 2015 Nat. Phys. 11 748
Google Scholar
[23] Liu Z K, Yang L X, Sun Y, Zhang T, Peng H, Yang H F, Chen C, Zhang Y, Guo Y, Prabhakaran D, Schmidt M, Hussain Z, Mo S K, Felser C, Yan B, Chen Y L 2016 Nat. Mater. 15 27
Google Scholar
[24] Belopolski I, Xu S Y, Sanchez D S, Chang G, Guo C, Neupane M, Zheng H, Lee C C, Huang S M, Bian G, Alidoust N, Chang T R, Wang B, Zhang X, Bansil A, Jeng H T, Lin H, Jia S, Hasan M Z 2016 Phys. Rev. Lett. 116 066802
Google Scholar
[25] Tanaka H, Telegin A V, Sukhorukov Y P, Golyashov V A, Tereshchenko O E, Lavrov A N, Matsuda T, Matsunaga R, Akashi R, Lippmaa M, Arai Y, Ideta S, Tanaka K, Kondo T, Kuroda K 2023 Phys. Rev. Lett. 130 186402
Google Scholar
[26] Tang S, Zhang C, Wong D, Pedramrazi Z, Tsai H Z, Jia C, Moritz B, Claassen M, Ryu H, Kahn S, Jiang J, Yan H, Hashimoto M, Lu D, Moore R G, Hwang C C, Hwang C, Hussain Z, Chen Y, Ugeda M M, Liu Z, Xie X, Devereaux T P, Crommie M F, Mo S K, Shen Z X 2017 Nat. Phys. 13 683
Google Scholar
[27] Schmitt F, Kirchmann P S, Bovensiepen U, Moore R G, Rettig L, Krenz M, Chu J H, Ru N, Perfetti L, Lu D H, Wolf M, Fisher I R, Shen Z X 2008 Science 321 1649
Google Scholar
[28] Rohwer T, Hellmann S, Wiesenmayer M, Sohrt C, Stange A, Slomski B, Carr A, Liu Y, Avila L M, Kalläne M, Mathias S, Kipp L, Rossnagel K, Bauer M 2011 Nature 471 490
Google Scholar
[29] Wang Y, Hsieh D, Sie E, Steinberg H, Gardner D, Lee Y, Jarillo-Herrero P, Gedik N 2012 Phys. Rev. Lett. 109 127401
Google Scholar
[30] Ossiander M, Riemensberger J, Neppl S, Mittermair M, Schäffer M, Duensing A, Wagner M S, Heider R, Wurzer M, Gerl M, Schnitzenbaumer M, Barth J V, Libisch F, Lemell C, Burgdörfer J, Feulner P, Kienberger R 2018 Nature 561 374
Google Scholar
[31] Fan H 1945 Phys. Rev. 68 43
Google Scholar
[32] Berglund C N, Spicer W E 1964 Phys. Rev. 136 A1030
Google Scholar
[33] Damascelli A 2004 Phys. Scr. 2004 61
Google Scholar
[34] Strocov V 2003 J. Electron Spectrosc. Relat. Phenom. 130 65
Google Scholar
[35] Seah M P, Dench W 1979 Surf. Interface Anal. 1 2
Google Scholar
[36] Strocov V, Starnberg H, Nilsson P, Brauer H, Holleboom L 1997 Phys. Rev. Lett. 79 467
Google Scholar
[37] Strocov V N, Shi M, Kobayashi M, Monney C, Wang X, Krempasky J, Schmitt T, Patthey L, Berger H, Blaha P 2012 Phys. Rev. Lett. 109 086401
Google Scholar
[38] Leemann S, Liu S, Hexemer A, Marcus M, Melton C, Nishimura H, Sun C 2019 Phys. Rev. Lett. 123 194801
Google Scholar
[39] Goodman J, King M, Dolier E, Wilson R, Gray R, McKenna P 2023 High Power Laser Sci. Eng. 11 e34
Google Scholar
[40] Pan D, Fan J, Nie Z, Sun Z, Zhang J, Tong Y, He B, Song C, Kohmura Y, Yabashi M, Ishikawa T, Shen Y, Jiang H 2022 IUCrJ 9 223
Google Scholar
[41] Zhou Z, Li C, Bi X, Zhang C, Huang Y, Zhuang J, Hua W, Dong Z, Zhao L, Zhang Y, Dong Y 2023 npj Comput. Mater. 9 58
Google Scholar
[42] Asahara A, Morita H, Ono K, Mitsumata C, Yano M, Shoji T 2019 Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence 33 9410
Google Scholar
[43] Chang M C, Wei Y, Chen W R, Do C 2020 MRS Commun. 10 11
Google Scholar
[44] Belič I, Poniku B, Jenko M 2012 Surf. Interface Anal. 44 1141
Google Scholar
[45] Yoon T, Kim S W, Byun H, Kim Y, Carter C D, Do H 2023 Combust. Flame 248 112583
Google Scholar
[46] Planckaert N, Demeulemeester J, Laenens B, Smeets D, Meersschaut J, L’abbé C, Temst K, Van-tomme A 2010 J. Synchrot. Radiat. 17 86
Google Scholar
[47] Martini A, Guda S, Guda A, Smolentsev G, Algasov A, Usoltsev O, Soldatov M, Bugaev A, Rusalev Y, Lamberti C, Soldatov A 2020 Comput. Phys. Commun. 250 107064
Google Scholar
[48] Roch L M, Saikin S K, Hase F, Friederich P, Goldsmith R H, León S, Aspuru-Guzik A 2020 ACS Nano 14 6589
Google Scholar
[49] Scarborough N M, Godaliyadda G M D P, Ye D H, Kissick D J, Zhang S, Newman J A, Sheedlo M J, Chowdhury A U, Fischetti R F, Das C, Buzzard G T, Bouman C A, Simpson G J 2017 J. Synchrot. Radiat. 24 188
Google Scholar
[50] Ke T W, Brewster A S, Yu S X, Ushizima D, Yang C, Sauter N K 2018 J. Synchrot. Radiat. 25 655
Google Scholar
[51] Sullivan B, Archibald R, Azadmanesh J, Vandavasi V G, Langan P S, Coates L, Lynch V, Langan P 2019 J. Appl. Crystallogr. 52 854
Google Scholar
[52] Lolla S, Liang H, Kusne A G, Takeuchi I, Ratcliff W 2022 J. Appl. Crystallogr. 55 882
Google Scholar
[53] Boulle A, Debelle A 2023 Mach. Learn.: Sci. Technol. 4 015002
Google Scholar
[54] Zhao C, Yu W, Li L 2023 Mater. Des. 228 111828
Google Scholar
[55] Kopp R, Joseph J, Ni X, Roy N, Wardle B L 2022 Adv. Mater. 34 2107817
Google Scholar
[56] Hendriksen A A, Bührer M, Leone L, Merlini M, Vigano N, Pelt D M, Marone F, Di Michiel M, Batenburg K J 2021 Sci Rep 11 11895
Google Scholar
[57] Huang D, Liu J, Qian T, Yang Y F 2023 Sci. China Phys. Mech. Astron. 66 267011
Google Scholar
[58] Pelzer K, Schwarz N, Harder R 2021 J. Appl. Crystallogr. 54 523
Google Scholar
[59] Thakur R S, Chatterjee S, Yadav R N, Gupta L 2021 IEEE Access 9 93338
Google Scholar
[60] Kim Y, Oh D, Huh S, Song D, Jeong S, Kwon J, Kim M, Kim D, Ryu H, Jung J, Kyung W, Sohn B, Lee S, Hyun J, Lee Y, Kim Y, Kim C 2021 Rev. Sci. Instrum. 92 073901
Google Scholar
[61] Restrepo F, Zhao J, Chatterjee U 2022 Rev. Sci. Instrum. 93 065106
Google Scholar
[62] Liu J, Huang D, Yang Y F, Qian T 2023 Phys. Rev. B 107 165106
Google Scholar
[63] Sun E 2022 IEEE MIT Undergraduate Research Technology Conference (URTC) Cambridge, MA, USA, 30 September 2022–02 October 2022, p1
[64] Iwasawa H, Ueno T, Masui T, Tajima S 2022 npj Quantum Mater. 7 24
Google Scholar
[65] Melton C N, Noack M M, Ohta T, Beechem T E, Robinson J, Zhang X, Bostwick A, Jozwiak C, Koch R J, Zwart P H, Hexemer A, Rotenberg E 2020 Mach. Learn.: Sci. Technol. 1 045015
Google Scholar
[66] Ekahana S A, Winata G I, Soh Y, Tamai A, Milan R, Aeppli G, Shi M 2023 Mach. Learn.: Sci. Technol. 4 035021
Google Scholar
[67] Park S H, Park H, Lee H, Kim H S 2021 J. Korean Phys. Soc. 79 1199
Google Scholar
[68] Pielsticker L, Nicholls R L, DeBeer S, Greiner M 2023 Anal. Chim. Acta 1271 341433
Google Scholar
[69] Xian R P, Stimper V, Zacharias M, Dendzik M, Dong S, Beaulieu S, Schölkopf B, Wolf M, Rettig L, Carbogno C, Bauer S, Ernstorfer R 2023 Nat. Comput. Sci. 3 101
Google Scholar
[70] Norman M, Eschrig M, Kaminski A, Campuzano J 2001 Phys. Rev. B 64 184508
Google Scholar
[71] Zhang H, Pincelli T, Jozwiak C, Kondo T, Ernstorfer R, Sato T, Zhou S 2022 Nat. Rev. Method. Prim. 2 54
Google Scholar
[72] Iwasawa H, Yoshida Y, Hase I, Shimada K, Namatame H, Taniguchi M, Aiura Y 2013 Sci. Rep. 3 1930
Google Scholar
[73] Yamaji Y, Yoshida T, Fujimori A, Imada M 2021 Phys. Rev. Res. 3 043099
Google Scholar
[74] Hohenberg P, Kohn W 1964 Phys. Rev. 13 6
Google Scholar
[75] Kohn W, Sham L J 1965 Phys. Rev. 140 A1133
Google Scholar
[76] Perdew J P, Burke K, Ernzerhof M 1996 Phys. Rev. Lett. 77 3865
Google Scholar
[77] Heyd J, Scuseria G E, Ernzerhof M 2003 J. Chem. Phys. 118 8207
Google Scholar
[78] Zhu X, Louie S G 1991 Phys. Rev. B 43 14142
Google Scholar
[79] Zanolli Z, Fuchs F, Furthmüller J, von Barth U, Bechstedt F 2007 Phys. Rev. B 75 245121
Google Scholar
[80] Aryasetiawan F, Gunnarsson O 1998 Rep. Prog. Phys. 61 237
Google Scholar
[81] Reining L 2018 Wiley Interdiscip. Rev.-Comput. Mol. Sci. 8 e1344
Google Scholar
[82] Golze D, Dvorak M, Rinke P 2019 Front. Chem. 7 377
Google Scholar
[83] Anisimov V I, Zaanen J, Andersen O K 1991 Phys. Rev. B 44 943
Google Scholar
[84] Dudarev S L, Botton G A, Savrasov S Y, Humphreys C, Sutton A P 1998 Phys. Rev. B 57 1505
Google Scholar
[85] Yu M, Yang S, Wu C, Marom N 2020 npj Comput. Mater. 6 180
Google Scholar
[86] Harun K, Salleh N A, Deghfel B, Yaakob M K, Mohamad A A 2020 Results Phys. 16 102829
Google Scholar
[87] Cococcioni M, De Gironcoli S 2005 Phys. Rev. B 71 035105
Google Scholar
[88] Kulik H J, Cococcioni M, Scherlis D A, Marzari N 2006 Phys. Rev. Lett. 97 103001
Google Scholar
[89] Mosey N J, Carter E A 2007 Phys. Rev. B 76 155123
Google Scholar
[90] Mosey N J, Liao P, Carter E A 2008 J. Chem. Phys. 129 014103
Google Scholar
[91] Aryasetiawan F, Karlsson K, Jepsen O, Schönberger U 2006 Phys. Rev. B 74 125106
Google Scholar
[92] Miyake T, Aryasetiawan F 2008 Phys. Rev. B 77 085122
Google Scholar
[93] Şaşıoğlu E, Friedrich C, Blügel S 2011 Phys. Rev. B 83 121101
Google Scholar
[94] Setvin M, Franchini C, Hao X, Schmid M, Janotti A, Kaltak M, Van de Walle C G, Kresse G, Diebold U 2014 Phys. Rev. Lett. 113 086402
Google Scholar
[95] Falletta S, Pasquarello A 2022 npj Comput. Mater. 8 263
Google Scholar
[96] Tavadze P, Boucher R, Avendaño-Franco G, Kocan K X, Singh S, Dovale-Farelo V, Ibarra-Hernández W, Johnson M B, Mebane D S, Romero A H 2021 npj Comput. Mater. 7 182
Google Scholar
[97] Golze D, Hirvensalo M, Hernández-León P, Aarva A, Etula J, Susi T, Rinke P, Laurila T, Caro M A 2022 Chem. Mat. 34 6240
Google Scholar
[98] Sun Q, Xiang Y, Liu Y, Xu L, Leng T, Ye Y, Fortunelli A, Goddard Ⅲ W A, Cheng T 2022 J. Phys. Chem. Lett. 13 8047
Google Scholar
[99] Yang S, Schröter N B M, Strocov V N, Schuwalow S, Rajpalk M, Ohtani K, Krogstrup P, Winkler G W, Gukelberger J, Gresch D, Aeppli G, Lutchyn R M, Marom N 2022 Adv. Quantum Technol. 5 2100033
Google Scholar
[100] Jardine M J A, Dardzinski D, Yu M, Purkayastha A, Chen A H, Chang Y H, Engel A, Strocov V N, Hocevar M, Palmstrom C, Frolov S M, Marom N 2023 ACS Appl. Mater. Interfaces 15 16288
Google Scholar
[101] Bubert H, Hillig H 2000 Microchim. Acta 133 95
Google Scholar
[102] Kim B, Kim W S 2007 Microelectron. Eng. 84 584
Google Scholar
[103] Kim B, Kim G T, Lee H J 2008 Mater. Manuf. Process. 23 528
Google Scholar
[104] Kim B, Kim J, Choi S 2009 Expert Syst. Appl. 36 11347
Google Scholar
[105] Englert T, Gruber F, Stiedl J, Green S, Jacob T, Rebner K, Grählert W 2021 Sensors 21 5595
Google Scholar
[106] Drera G, Kropf C M, Sangaletti L 2020 Mach. Learn.: Sci. Technol. 1 015008
Google Scholar
[107] Baker N, Alexander F, Bremer T, Hagberg A, Kevrekidis Y, Najm H, Parashar M, Patra A, Sethian J, Wild S, Willcox K, Lee S 2019 Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence (Washington DC, United States: USDOE Office of Science (SC)) 1478744
[108] Choudhary K, DeCost B, Chen C, Jain A, Tavazza F, Cohn R, Park C W, Choudhary A, Agrawal A, Billinge S J L, Holm E, Ong S P, Wolverton C 2022 npj Comput. Mater. 8 59
Google Scholar
[109] Cranmer M, Sanchez-Gonzalez A, Battaglia P, Xu R, Cranmer K, Spergel D, Ho S 2020 NIPS ’20: Proceedings of the 34th International Conference on Neural Information Processing Systems (Vancouver, BC, Canada: Curran Associates Inc.) p17429
[110] Cranmer M, Greydanus S, Hoyer S, Battaglia P, Spergel D, Ho S 2020 arXiv: 2003.04630 [physics.comp-ph]
[111] Samarakoon A M, Laurell P, Balz C, Banerjee A, Lampen-Kelley P, Mandrus D, Nagler S E, Okamoto S, Tennant D A 2022 Phys. Rev. Res. 4 L022061
Google Scholar
[112] Schütt K T, Sauceda H E, Kindermans P J, Tkatchenko A, Müller K R 2018 J. Chem. Phys. 148 241722
Google Scholar
[113] Sobral J A, Obernauer S, Turkel S, Pasupathy A N, Scheurer M S 2023 Nat. Commun. 14 5012
Google Scholar
[114] Chen Z, Andrejevic N, Drucker N C, Nguyen T, Xian R P, Smidt T, Wang Y, Ernstorfer R, Tennant D A, Chan M, Li M 2021 Chem. Phys. Rev. 2 031301
Google Scholar
[115] Doucet M, Samarakoon A M, Do C, Heller W T, Archibald R, Tennant D A, Proffen T, Granroth G E 2020 Mach. Learn.: Sci. Technol. 2 023001
Google Scholar
[116] Chitturi S R, Ratner D, Walroth R C, Thampy V, Reed E J, Dunne M, Tassone C J, Stone K H 2021 J. Appl. Crystallogr. 54 1799
Google Scholar
[117] Matsumura T, Nagamura N, Akaho S, Nagata K, Ando Y 2019 Sci. Technol. Adv. Mater. 20 733
Google Scholar
[118] Xi B, Tse K F, Kok T F, Chan H M, Chan M K, Chan H Y, Clinton Wong K Y, Robin Yuen S H, Zhu J 2022 J. Phys. Chem. C 126 12264
Google Scholar
[119] Bergstra J, Bengio Y 2012 J. Mach. Learn. Res. 13 281
Google Scholar
[120] Bergstra J, Bardenet R, Bengio Y, Kégl B 2011 Proceedings of the 24th International Conference on Neural Information Processing Systems (Vol. 24 of NIPS’11) (Granada: Curran Associates, Inc.) p2546
[121] Gardner J R, Kusner M J, Xu Z E, Weinberger K Q, Cunningham J P 2014 Proceedings of the 31st International Conference on International Conference on Machine Learning (Vol. 32 of ICML’14) (Beijing, China: JMLR.org) p II-937
[122] Bergstra J, Yamins D, Cox D 2013 Procedings of the 30 th International Conference on Machine Learning (Vol. 28 of ICML’13) (Atlanta, GA, USA: JMLR.org) p I-115
[123] Akiba T, Sano S, Yanase T, Ohta T, Koyama M 2019 Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Vol. 18 of KDD ’19) (Anchorage, AK, USA: ACM) p2623
[124] Kvasnicka V, Sklenak S, Pospichal J 1992 J. Chem. Inf. Comput. Sci. 32 742
Google Scholar
[125] Simine L, Allen T C, Rossky P J 2020 Proc. Natl. Acad. Sci. 117 13945
Google Scholar
[126] Urbina F, Batra K, Luebke K J, White J D, Matsiev D, Olson L L, Malerich J P, Hupcey M A, Madrid P B, Ekins S 2021 Anal. Chem. 93 16076
Google Scholar
[127] Wu X, Zhao Z, Tian R, Niu Y, Gao S, Liu H 2021 Spectroc. Acta Pt. A: Molec. Biomolec. Spectr. 244 118841
Google Scholar
[128] Scarselli F, Gori M, Tsoi A C, Hagenbuchner M, Monfardini G 2009 IEEE Trans. Neural Netw. 20 61
Google Scholar
[129] Coley C W, Jin W, Rogers L, Jamison T F, Jaakkola T S, Green W H, Barzilay R, Jensen K F 2019 Chem. Sci. 10 370
Google Scholar
[130] Stärk H, Beaini D, Corso G, Tossou P, Dallago C, Günnemann S, Lió P 2022 Proceedings of the 39th International Conference on Machine Learning (Vol. 162 of Proceedings of Machine Learning Research) (Baltimore, MD, USA: PMLR) p20479
[131] Xie T, Grossman J C 2018 Phys. Rev. Lett. 120 145301
Google Scholar
[132] Gao W, Mahajan S P, Sulam J, Gray J J 2020 Patterns 1 100142
Google Scholar
[133] Choudhary K, DeCost B 2021 npj Comput. Mater. 7 185
Google Scholar
[134] Bang K, Yeo B C, Kim D, Han S S, Lee H M 2021 Sci. Rep. 11 11604
Google Scholar
[135] Kong S, Ricci F, Guevarra D, Neaton J B, Gomes C P, Gregoire J M 2022 Nat. Commun. 13 949
Google Scholar
[136] Fung V, Ganesh P, Sumpter B G 2022 Chem. Mat. 34 4848
Google Scholar
[137] Kaundinya P R, Choudhary K, Kalidindi S R 2022 JOM 74 1395
Google Scholar
[138] Masood H, Sirojan T, Toe C Y, Kumar P V, Haghshenas Y, Sit P H, Amal R, Sethu V, Teoh W Y 2023 Cell Rep. Phys. Sci. 4 101555
Google Scholar
[139] Lee J, Asahi R 2021 Comput. Mater. Sci. 190 110314
Google Scholar
[140] Li B, Rangarajan S 2022 Comput. Chem. Eng. 157 107599
Google Scholar
[141] Tian S I P, Ren Z, Venkataraj S, Cheng Y, Bash D, Oviedo F, Senthilnath J, Chellappan V, Lim Y F, Aberle A G, MacLeod B P, Parlane F G L, Berlinguette C P, Li Q, Buonassisi T, Liu Z 2023 Digit. Discov. 2 1334
Google Scholar
[142] Zuo H, Zhang G, Pedrycz W, Behbood V, Lu J 2016 IEEE Trans. Fuzzy Syst. 25 1795
Google Scholar
[143] Wang L, Zhang C, Bai R, Li J, Duan H 2020 Chem. Commun. 56 9368
Google Scholar
[144] Yamada H, Liu C, Wu S, Koyama Y, Ju S, Shiomi J, Morikawa J, Yoshida R 2019 ACS Central Sci. 5 1717
Google Scholar
[145] Pan S J, Yang Q 2009 IEEE Trans. Knowl. Data Eng. 22 1345
Google Scholar
[146] Xu P, Ji X, Li M, Lu W 2023 npj Comput. Mater. 9 42
Google Scholar
下载:
计量
- 文章访问数: 11417
- PDF下载量: 263
- 被引次数: 0
