{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T16:22:23Z","timestamp":1773850943663,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2022,2]]},"DOI":"10.1007\/s00500-021-06533-3","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T22:02:56Z","timestamp":1638396176000},"page":"1181-1196","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Training data augmentation using generative models with statistical guarantees for materials informatics"],"prefix":"10.1007","volume":"26","author":[{"given":"Hiroshi","family":"Ohno","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,1]]},"reference":[{"key":"6533_CR1","unstructured":"Arjovsky M, Bottou L (2017) Towards principled methods for training generative adversarial networks. In: Proceedings of the 5th International Conference on Learning Representations"},{"key":"6533_CR2","volume-title":"Pattern recognition and machine learning","author":"CM Bishop","year":"2006","unstructured":"Bishop CM (2006) Pattern recognition and machine learning. Springer, New Yrok"},{"key":"6533_CR3","doi-asserted-by":"publisher","first-page":"17953","DOI":"10.1103\/PhysRevB.50.17953","volume":"50","author":"PE Bl\u00f6chl","year":"1994","unstructured":"Bl\u00f6chl PE (1994) Projector augmented-wave method. Phys Rev B 50:17953\u201317979","journal-title":"Phys Rev B"},{"issue":"21","key":"6533_CR4","doi-asserted-by":"publisher","first-page":"214701","DOI":"10.1063\/1.5093220","volume":"150","author":"ED Cubuk","year":"2019","unstructured":"Cubuk ED, Sendek AD, Reed EJ (2019) Screening billions of candidates for solid lithium-ion conductors: a transfer learning approach for small data. J Chem Phys 150(21):214701","journal-title":"J Chem Phys"},{"issue":"7","key":"6533_CR5","doi-asserted-by":"publisher","first-page":"3187","DOI":"10.1109\/TII.2018.2822680","volume":"14","author":"Z Cui","year":"2018","unstructured":"Cui Z, Xue F, Cai X, Cao Y, Wang Gg, Chen J (2018) Detection of malicious code variants based on deep learning. IEEE Trans Ind Inf 14(7):3187\u20133196. https:\/\/doi.org\/10.1109\/TII.2018.2822680","journal-title":"IEEE Trans Ind Inf"},{"key":"6533_CR6","unstructured":"Danihelka I, Lakshminarayanan B, Uria B, Wierstra D, Dayan P (2017) Comparison of maximum likelihood and GAN-based training of real NVPs. CoRR abs\/1705.05263"},{"key":"6533_CR7","unstructured":"Dinh L, Sohl-Dickstein J, Bengio S (2017) Density estimation using real NVP. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings, OpenReview.net"},{"key":"6533_CR8","unstructured":"Gatys LA, Ecker AS, Bethge M (2015) A neural algorithm of artistic style. CoRR abs\/1508.06576"},{"key":"6533_CR9","doi-asserted-by":"publisher","first-page":"105503","DOI":"10.1103\/PhysRevLett.114.105503","volume":"114","author":"LM Ghiringhelli","year":"2015","unstructured":"Ghiringhelli LM, Vybiral J, Levchenko SV, Draxl C, Scheffler M (2015) Big data of materials science: critical role of the descriptor. Phys Rev Lett 114:105503","journal-title":"Phys Rev Lett"},{"key":"6533_CR10","first-page":"2672","volume-title":"Advances in neural information processing systems 27","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ (eds) Advances in neural information processing systems 27. Curran Associates Inc., New York, pp 2672\u20132680"},{"key":"6533_CR11","doi-asserted-by":"crossref","unstructured":"Gurumurthy S, Sarvadevabhatla RK, Babu RV (2017) Deligan: Generative adversarial networks for diverse and limited data. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, IEEE Computer Society, pp 4941\u20134949","DOI":"10.1109\/CVPR.2017.525"},{"issue":"16","key":"6533_CR12","doi-asserted-by":"publisher","first-page":"10936","DOI":"10.1021\/acs.inorgchem.9b01370","volume":"58","author":"H Hazama","year":"2019","unstructured":"Hazama H, Sobue S, Tajima S, Asahi R (2019) Phosphorescent material search using a combination of high-throughput evaluation and machine learning. Inorg Chem 58(16):10936\u201310943","journal-title":"Inorg Chem"},{"issue":"16","key":"6533_CR13","doi-asserted-by":"publisher","first-page":"4562","DOI":"10.1021\/acs.jpclett.8b01707","volume":"9","author":"Y He","year":"2018","unstructured":"He Y, Cubuk ED, Allendorf MD, Reed EJ (2018) Metallic metal-organic frameworks predicted by the combination of machine learning methods and ab initio calculations. J Phys Chem Lett 9(16):4562\u20134569","journal-title":"J Phys Chem Lett"},{"key":"6533_CR14","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","volume":"97","author":"AA Heidari","year":"2019","unstructured":"Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Future Gener Comput Syst 97:849\u2013872. https:\/\/doi.org\/10.1016\/j.future.2019.02.028","journal-title":"Future Gener Comput Syst"},{"issue":"1","key":"6533_CR15","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1038\/s41427-020-0211-1","volume":"12","author":"S Kajita","year":"2020","unstructured":"Kajita S, Ohba N, Suzumura A, Tajima S, Asahi R (2020) Discovery of superionic conductors by ensemble-scope descriptor. NPG Asia Mater 12(1):31","journal-title":"NPG Asia Mater"},{"key":"6533_CR16","doi-asserted-by":"publisher","first-page":"11169","DOI":"10.1103\/PhysRevB.54.11169","volume":"54","author":"G Kresse","year":"1996","unstructured":"Kresse G, Furthm\u00fcller J (1996) Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys Rev B 54:11169\u201311186","journal-title":"Phys Rev B"},{"key":"6533_CR17","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.future.2020.03.055","volume":"111","author":"S Li","year":"2020","unstructured":"Li S, Chen H, Wang M, Heidari AA, Mirjalili S (2020) Slime mould algorithm: a new method for stochastic optimization. Future Gener Comput Syst 111:300\u2013323. https:\/\/doi.org\/10.1016\/j.future.2020.03.055","journal-title":"Future Gener Comput Syst"},{"key":"6533_CR18","doi-asserted-by":"crossref","unstructured":"Lukasik S (2007) Parallel computing of kernel density estimates with mpi. In: International Conference on Computational Science","DOI":"10.1007\/978-3-540-72588-6_120"},{"key":"6533_CR19","unstructured":"Mariani G, Scheidegger F, Istrate R, Bekas C, Malossi ACI (2018) BAGAN: data augmentation with balancing GAN. CoRR abs\/1803.09655"},{"issue":"1","key":"6533_CR20","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1038\/s43246-019-0004-7","volume":"1","author":"M Matsubara","year":"2020","unstructured":"Matsubara M, Suzumura A, Ohba N, Asahi R (2020) Identifying superionic conductors by materials informatics and high-throughput synthesis. Commun Mater 1(1):5","journal-title":"Commun Mater"},{"key":"6533_CR21","unstructured":"Mirza M, Osindero S (2014) Conditional generative adversarial nets. CoRR abs\/1411.1784"},{"key":"6533_CR22","doi-asserted-by":"publisher","first-page":"105932","DOI":"10.1016\/j.asoc.2019.105932","volume":"86","author":"H Ohno","year":"2019","unstructured":"Ohno H (2019) Training data augmentation: an empirical study using generative adversarial net-based approach with normalizing flow models for materials informatics. Appl Soft Comput 86:105932","journal-title":"Appl Soft Comput"},{"issue":"11","key":"6533_CR23","doi-asserted-by":"publisher","first-page":"7999","DOI":"10.1007\/s00500-019-04094-0","volume":"24","author":"H Ohno","year":"2020","unstructured":"Ohno H (2020) Auto-encoder-based generative models for data augmentation on regression problems. Soft Comput 24(11):7999\u20138009","journal-title":"Soft Comput"},{"key":"6533_CR24","doi-asserted-by":"publisher","first-page":"094106","DOI":"10.1103\/PhysRevB.97.094106","volume":"97","author":"B Onat","year":"2018","unstructured":"Onat B, Cubuk ED, Malone BD, Kaxiras E (2018) Implanted neural network potentials: application to li-si alloys. Phys Rev B 97:094106","journal-title":"Phys Rev B"},{"key":"6533_CR25","unstructured":"P\u00a0Kingma D, Welling M (2014) Auto-encoding variational Bayes. In: Proceedings of the 2nd International Conference on Learning Representations"},{"key":"6533_CR26","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1198\/jcgs.2010.09046","volume":"19","author":"V Raykar","year":"2010","unstructured":"Raykar V, Duraiswami R, Zhao L (2010) Fast computation of kernel estimators. J Comput Graph Stat 19:205\u2013220","journal-title":"J Comput Graph Stat"},{"key":"6533_CR27","doi-asserted-by":"publisher","first-page":"104104","DOI":"10.1103\/PhysRevB.85.104104","volume":"85","author":"Y Saad","year":"2012","unstructured":"Saad Y, Gao D, Ngo T, Bobbitt S, Chelikowsky JR, Andreoni W (2012) Data mining for materials: computational experiments with $${AB}$$ compounds. Phys Rev B 85:104104","journal-title":"Phys Rev B"},{"issue":"12","key":"6533_CR28","doi-asserted-by":"publisher","first-page":"121101","DOI":"10.1063\/1.4971801","volume":"4","author":"S Sanna","year":"2016","unstructured":"Sanna S, Esposito V, Christensen M, Pryds N (2016) High ionic conductivity in confined bismuth oxide-based heterostructures. APL Mater 4(12):121101","journal-title":"APL Mater"},{"key":"6533_CR29","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1039\/C6EE02697D","volume":"10","author":"AD Sendek","year":"2017","unstructured":"Sendek AD, Yang Q, Cubuk ED, Duerloo KAN, Cui Y, Reed EJ (2017) Holistic computational structure screening of more than 12000 candidates for solid lithium-ion conductor materials. Energy Environ Sci 10:306\u2013320","journal-title":"Energy Environ Sci"},{"issue":"1","key":"6533_CR30","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6(1):60","journal-title":"J Big Data"},{"key":"6533_CR31","first-page":"5","volume-title":"Cambridge series in statistical and probabilistic mathematics","author":"R Vershynin","year":"2018","unstructured":"Vershynin R (2018) High-dimensional probability: an introduction with applications in data science. Cambridge series in statistical and probabilistic mathematics. Cambridge University Press, Cambridge, pp 5\u201355"},{"key":"6533_CR32","doi-asserted-by":"publisher","first-page":"632437","DOI":"10.1155\/2013\/632437","volume":"2013","author":"G Wang","year":"2013","unstructured":"Wang G, Guo L, Duan H (2013) Wavelet neural network using multiple wavelet functions in target threat assessment. Sci World J 2013:632437. https:\/\/doi.org\/10.1155\/2013\/632437","journal-title":"Sci World J"},{"key":"6533_CR33","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/s00521-015-1874-3","volume":"27","author":"G Wang","year":"2015","unstructured":"Wang G, Lu M, Dong YQ, Zhao X (2015a) Self-adaptive extreme learning machine. Neural Comput Appl 27:291\u2013303","journal-title":"Neural Comput Appl"},{"issue":"2","key":"6533_CR34","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/s12293-016-0212-3","volume":"10","author":"GG Wang","year":"2018","unstructured":"Wang GG (2018) Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memet Comput 10(2):151\u2013164. https:\/\/doi.org\/10.1007\/s12293-016-0212-3","journal-title":"Memet Comput"},{"key":"6533_CR35","doi-asserted-by":"crossref","unstructured":"Wang GG, Deb S, Coelho LdS (2015b) Elephant herding optimization. In: 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI), pp 1\u20135, 10.1109\/ISCBI.2015.8","DOI":"10.1109\/ISCBI.2015.8"},{"key":"6533_CR36","doi-asserted-by":"crossref","unstructured":"Wang GG, Deb S, Coelho LDS (2018) International Journal of Bio-Inspired Computation 12(1):1\u201322","DOI":"10.1504\/IJBIC.2018.093328"},{"issue":"7","key":"6533_CR37","doi-asserted-by":"publisher","first-page":"1995","DOI":"10.1007\/s00521-015-1923-y","volume":"31","author":"GG Wang","year":"2019","unstructured":"Wang GG, Deb S, Cui Z (2019) Monarch butterfly optimization. Neural Comput Appl 31(7):1995\u20132014. https:\/\/doi.org\/10.1007\/s00521-015-1923-y","journal-title":"Neural Comput Appl"},{"key":"6533_CR38","unstructured":"Wu Y, Burda Y, Salakhutdinov R, Grosse RB (2016) On the quantitative analysis of decoder-based generative models. CoRR abs\/1611.04273"},{"key":"6533_CR39","first-page":"65","volume-title":"NatureInspired cooperative strategies for optimization (NICSO 2010), SCI, New York, NY","author":"Y Xin-she","year":"2010","unstructured":"Xin-she Y (2010) A new metaheuristic bat-inspired algorithm. NatureInspired cooperative strategies for optimization (NICSO 2010), SCI, New York, NY. Springer, USA, pp 65\u201374"},{"issue":"1","key":"6533_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1177\/1687814015624832","volume":"8","author":"JH Yi","year":"2016","unstructured":"Yi JH, Wang J, Wang GG (2016) Improved probabilistic neural networks with self-adaptive strategies for transformer fault diagnosis problem. Adv Mech Eng 8(1):1\u201313. https:\/\/doi.org\/10.1177\/1687814015624832","journal-title":"Adv Mech Eng"},{"key":"6533_CR41","doi-asserted-by":"publisher","first-page":"29857","DOI":"10.1109\/ACCESS.2020.2972859","volume":"8","author":"S Zhang","year":"2020","unstructured":"Zhang S, Zhang S, Wang B, Habetler TG (2020) Deep learning algorithms for bearing fault diagnostics-a comprehensive review. IEEE Access 8:29857\u201329881","journal-title":"IEEE Access"},{"key":"6533_CR42","doi-asserted-by":"publisher","first-page":"107377","DOI":"10.1016\/j.measurement.2019.107377","volume":"152","author":"W Zhang","year":"2020","unstructured":"Zhang W, Li X, Jia XD, Ma H, Luo Z, Li X (2020) Machinery fault diagnosis with imbalanced data using deep generative adversarial networks. Measurement 152:107377","journal-title":"Measurement"},{"issue":"1","key":"6533_CR43","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/s41524-018-0081-z","volume":"4","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Ling C (2018) A strategy to apply machine learning to small datasets in materials science. npj Comput Mater 4(1):25","journal-title":"npj Comput Mater"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-021-06533-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-021-06533-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-021-06533-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,24]],"date-time":"2022-01-24T16:17:49Z","timestamp":1643041069000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-021-06533-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,1]]},"references-count":43,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,2]]}},"alternative-id":["6533"],"URL":"https:\/\/doi.org\/10.1007\/s00500-021-06533-3","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,1]]},"assertion":[{"value":"1 November 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 December 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declrations"}},{"value":"The author declares that there are no conflicts of interest regarding the publication of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by the author.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animals rights"}}]}}