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引用“”的文章Machine learning phases of matter

标题/作者	年份
Quantum machine learning J Biamonte, P Wittek, N Pancotti, P Rebentrost, N Wiebe, S Lloyd Nature 549 (7671), 195-202	2017
Machine learning for molecular and materials science KT Butler, DW Davies, H Cartwright, O Isayev, A Walsh Nature 559 (7715), 547-555	2018
Solving the quantum many-body problem with artificial neural networks G Carleo, M Troyer Science 355 (6325), 602-606	2017
Machine learning and the physical sciences G Carleo, I Cirac, K Cranmer, L Daudet, M Schuld, N Tishby, ... Reviews of Modern Physics 91 (4), 045002	2019
Recent advances and applications of machine learning in solid-state materials science J Schmidt, MRG Marques, S Botti, MAL Marques npj Computational Materials 5 (1), 1-36	2019
Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy YK Dwivedi, L Hughes, E Ismagilova, G Aarts, C Coombs, T Crick, Y Duan, ... International Journal of Information Management 57, 101994	2021
A high-bias, low-variance introduction to machine learning for physicists P Mehta, M Bukov, CH Wang, AGR Day, C Richardson, CK Fisher, ... Physics reports 810, 1-124	2019
Learning phase transitions by confusion EPL Van Nieuwenburg, YH Liu, SD Huber Nature Physics 13 (5), 435-439	2017
Deep learning for cellular image analysis E Moen, D Bannon, T Kudo, W Graf, M Covert, D Van Valen Nature methods 16 (12), 1233-1246	2019
Machine learning & artificial intelligence in the quantum domain: a review of recent progress V Dunjko, HJ Briegel Reports on Progress in Physics 81 (7), 074001	2018
Neural-network quantum state tomography G Torlai, G Mazzola, J Carrasquilla, M Troyer, R Melko, G Carleo Nature Physics 14 (5), 447-450	2018
Deep-learning-enabled on-demand design of chiral metamaterials W Ma, F Cheng, Y Liu ACS nano 12 (6), 6326-6334	2018
Generative model for the inverse design of metasurfaces Z Liu, D Zhu, SP Rodrigues, KT Lee, W Cai Nano letters 18 (10), 6570-6576	2018
Quantum convolutional neural networks I Cong, S Choi, MD Lukin Nature Physics 15 (12), 1273-1278	2019
Quantum entanglement in neural network states DL Deng, X Li, SD Sarma Physical Review X 7 (2), 021021	2017
Supervised learning with tensor networks E Stoudenmire, DJ Schwab Advances in Neural Information Processing Systems 29	2016
Machine learning phases of strongly correlated fermions K Ch’Ng, J Carrasquilla, RG Melko, E Khatami Physical Review X 7 (3), 031038	2017
Unsupervised learning of phase transitions: From principal component analysis to variational autoencoders SJ Wetzel Physical Review E 96 (2), 022140	2017
From DFT to machine learning: recent approaches to materials science–a review GR Schleder, ACM Padilha, CM Acosta, M Costa, A Fazzio Journal of Physics: Materials 2 (3), 032001	2019
Discovering physical concepts with neural networks R Iten, T Metger, H Wilming, L Del Rio, R Renner Physical review letters 124 (1), 010508	2020

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