{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T08:34:48Z","timestamp":1762504488394,"version":"3.37.3"},"reference-count":62,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:00:00Z","timestamp":1701475200000},"content-version":"am","delay-in-days":304,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"Department of Energy","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000181","name":"Air Force Office of Scientific Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational Physics"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1016\/j.jcp.2022.111765","type":"journal-article","created":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T01:23:29Z","timestamp":1668216209000},"page":"111765","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":18,"special_numbering":"C","title":["Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation"],"prefix":"10.1016","volume":"474","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0043-8462","authenticated-orcid":false,"given":"Jeffmin","family":"Lin","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4857-6407","authenticated-orcid":false,"given":"Gil","family":"Goldshlager","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6860-9566","authenticated-orcid":false,"given":"Lin","family":"Lin","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.jcp.2022.111765_br0010","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1126\/science.aag2302","article-title":"Solving the quantum many-body problem with artificial neural networks","volume":"355","author":"Carleo","year":"2017","journal-title":"Science"},{"key":"10.1016\/j.jcp.2022.111765_br0020","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevB.96.205152","article-title":"Restricted Boltzmann machine learning for solving strongly correlated quantum systems","volume":"96","author":"Nomura","year":"2017","journal-title":"Phys. Rev. B"},{"key":"10.1016\/j.jcp.2022.111765_br0030","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevLett.121.167204","article-title":"Symmetries and many-body excitations with neural-network quantum states","volume":"121","author":"Choo","year":"2018","journal-title":"Phys. Rev. Lett."},{"key":"10.1016\/j.jcp.2022.111765_br0040","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevLett.122.250501","article-title":"Variational quantum Monte Carlo method with a neural-network ansatz for open quantum systems","volume":"122","author":"Nagy","year":"2019","journal-title":"Phys. Rev. Lett."},{"key":"10.1016\/j.jcp.2022.111765_br0050","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevLett.122.226401","article-title":"Backflow transformations via neural networks for quantum many-body wave functions","volume":"122","author":"Luo","year":"2019","journal-title":"Phys. Rev. Lett."},{"key":"10.1016\/j.jcp.2022.111765_br0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.jcp.2019.108929","article-title":"Solving many-electron Schr\u00f6dinger equation using deep neural networks","volume":"399","author":"Han","year":"2019","journal-title":"J. Comput. Phys."},{"key":"10.1016\/j.jcp.2022.111765_br0070","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevResearch.2.012039","article-title":"Deep learning-enhanced variational Monte Carlo method for quantum many-body physics","volume":"2","author":"Yang","year":"2020","journal-title":"Phys. Rev. Res."},{"key":"10.1016\/j.jcp.2022.111765_br0080","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1038\/s41557-020-0544-y","article-title":"Deep-neural-network solution of the electronic Schr\u00f6dinger equation","volume":"12","author":"Hermann","year":"2020","journal-title":"Nat. Chem."},{"key":"10.1016\/j.jcp.2022.111765_br0090","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevResearch.2.033429","article-title":"Ab initio solution of the many-electron Schr\u00f6dinger equation with deep neural networks","volume":"2","author":"Pfau","year":"2020","journal-title":"Phys. Rev. Res."},{"key":"10.1016\/j.jcp.2022.111765_br0100","doi-asserted-by":"crossref","first-page":"2368","DOI":"10.1038\/s41467-020-15724-9","article-title":"Fermionic neural-network states for ab-initio electronic structure","volume":"11","author":"Choo","year":"2020","journal-title":"Nat. Commun."},{"key":"10.1016\/j.jcp.2022.111765_br0110","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevB.102.205122","article-title":"Phases of two-dimensional spinless lattice fermions with first-quantized deep neural-network quantum states","volume":"102","author":"Stokes","year":"2020","journal-title":"Phys. Rev. B"},{"year":"1989","series-title":"Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory","author":"Szabo","key":"10.1016\/j.jcp.2022.111765_br0120"},{"key":"10.1016\/j.jcp.2022.111765_br0130","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1103\/PhysRev.102.1189","article-title":"Energy spectrum of the excitations in liquid helium","volume":"102","author":"Feynman","year":"1956","journal-title":"Phys. Rev."},{"key":"10.1016\/j.jcp.2022.111765_br0140","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevB.78.041101","article-title":"Role of backflow correlations for the nonmagnetic phase of the t\u2013t\u2032 Hubbard model","volume":"78","author":"Tocchio","year":"2008","journal-title":"Phys. Rev. B"},{"key":"10.1016\/j.jcp.2022.111765_br0150","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1103\/RevModPhys.73.33","article-title":"Quantum Monte Carlo simulations of solids","volume":"73","author":"Foulkes","year":"2001","journal-title":"Rev. Mod. Phys."},{"year":"2016","series-title":"Quantum Monte Carlo Methods","author":"Gubernatis","key":"10.1016\/j.jcp.2022.111765_br0160"},{"key":"10.1016\/j.jcp.2022.111765_br0170","series-title":"Electron Correlation in Molecules \u2013 Ab Initio Beyond Gaussian Quantum Chemistry, Advances in Quantum Chemistry, vol. 73","first-page":"285","article-title":"Chapter fifteen - introduction to the variational and diffusion Monte Carlo methods","author":"Toulouse","year":"2016"},{"year":"2017","series-title":"Quantum Monte Carlo Approaches for Correlated Systems","author":"Becca","key":"10.1016\/j.jcp.2022.111765_br0180"},{"author":"Spencer","key":"10.1016\/j.jcp.2022.111765_br0190"},{"author":"Han","key":"10.1016\/j.jcp.2022.111765_br0200"},{"author":"Sannai","key":"10.1016\/j.jcp.2022.111765_br0210"},{"key":"10.1016\/j.jcp.2022.111765_br0220","first-page":"7092","article-title":"Universal invariant and equivariant graph neural networks","volume":"32","author":"Keriven","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"year":"2020","series-title":"On Representing (Anti)Symmetric Functions","author":"Hutter","key":"10.1016\/j.jcp.2022.111765_br0230"},{"author":"Bachmayr","key":"10.1016\/j.jcp.2022.111765_br0240"},{"key":"10.1016\/j.jcp.2022.111765_br0250","first-page":"3391","article-title":"Deep sets","volume":"vol. 30","author":"Zaheer","year":"2017"},{"key":"10.1016\/j.jcp.2022.111765_br0260","doi-asserted-by":"crossref","DOI":"10.1002\/adts.202000269","article-title":"Artificial neural networks as trial wave functions for quantum Monte Carlo","volume":"4","author":"Kessler","year":"2021","journal-title":"Adv. Theory Simulations"},{"author":"Spencer","key":"10.1016\/j.jcp.2022.111765_br0270"},{"key":"10.1016\/j.jcp.2022.111765_br0280","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.jcp.2022.111765_br0290","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1002\/cpa.3160100201","article-title":"On the eigenfunctions of many-particle systems in quantum mechanics","volume":"10","author":"Kato","year":"1957","journal-title":"Commun. Pure Appl. Math."},{"key":"10.1016\/j.jcp.2022.111765_br0300","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/BF00993164","article-title":"Approximation and estimation bounds for artificial neural networks","volume":"14","author":"Barron","year":"1994","journal-title":"Mach. Learn."},{"key":"10.1016\/j.jcp.2022.111765_br0310","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1017\/S0962492900002919","article-title":"Approximation theory of the mlp model in neural networks","volume":"8","author":"Pinkus","year":"1999","journal-title":"Acta Numer."},{"key":"10.1016\/j.jcp.2022.111765_br0320","series-title":"International Conference on Learning Representations","article-title":"The power of deeper networks for expressing natural functions","author":"Rolnick","year":"2018"},{"author":"Elbr\u00e4chter","key":"10.1016\/j.jcp.2022.111765_br0330"},{"key":"10.1016\/j.jcp.2022.111765_br0340","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1007\/s10955-017-1836-5","article-title":"Why does deep and cheap learning work so well?","volume":"168","author":"Lin","year":"2017","journal-title":"J. Stat. Phys."},{"author":"Hendrycks","key":"10.1016\/j.jcp.2022.111765_br0350"},{"key":"10.1016\/j.jcp.2022.111765_br0360","series-title":"Advances in Chemical Physics","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1002\/9780470141649.ch3","article-title":"Between classical and quantum Monte Carlo methods: \u201cvariational\u201d qmc","author":"Bressanini","year":"1999"},{"key":"10.1016\/j.jcp.2022.111765_br0370","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1002\/qua.560120826","article-title":"Zero Monte Carlo error or quantum mechanics is easier","volume":"12","author":"Coldwell","year":"1977","journal-title":"Int. J. Quant. Chem."},{"key":"10.1016\/j.jcp.2022.111765_br0380","doi-asserted-by":"crossref","first-page":"1719","DOI":"10.1103\/PhysRevLett.60.1719","article-title":"Optimized trial wave functions for quantum Monte Carlo calculations","volume":"60","author":"Umrigar","year":"1988","journal-title":"Phys. Rev. Lett."},{"key":"10.1016\/j.jcp.2022.111765_br0390","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevB.59.12344","article-title":"Monte Carlo energy and variance-minimization techniques for optimizing many-body wave functions","volume":"59","author":"Kent","year":"1999","journal-title":"Phys. Rev. B"},{"key":"10.1016\/j.jcp.2022.111765_br0410","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevLett.94.150201","article-title":"Energy and variance optimization of many-body wave functions","volume":"94","author":"Umrigar","year":"2005","journal-title":"Phys. Rev. Lett."},{"key":"10.1016\/j.jcp.2022.111765_br0420","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevB.85.045103","article-title":"Optimizing large parameter sets in variational quantum Monte Carlo","volume":"85","author":"Neuscamman","year":"2012","journal-title":"Phys. Rev. B"},{"key":"10.1016\/j.jcp.2022.111765_br0430","doi-asserted-by":"crossref","DOI":"10.1039\/C9CP02269D","article-title":"Complementary first and second derivative methods for ansatz optimization in variational Monte Carlo","volume":"21","author":"Otis","year":"2019","journal-title":"Phys. Chem. Chem. Phys."},{"key":"10.1016\/j.jcp.2022.111765_br0440","doi-asserted-by":"crossref","DOI":"10.1063\/1.5125803","article-title":"An accelerated linear method for optimizing non-linear wavefunctions in variational Monte Carlo","volume":"152","author":"Sabzevari","year":"2020","journal-title":"J. Chem. Phys."},{"key":"10.1016\/j.jcp.2022.111765_br0450","first-page":"2408","article-title":"Optimizing neural networks with Kronecker-factored approximate curvature","volume":"vol. 37","author":"Martens","year":"2015"},{"key":"10.1016\/j.jcp.2022.111765_br0460","series-title":"International Conference on Learning Representations","article-title":"Adam: a method for stochastic optimization","author":"Kingma","year":"2015"},{"key":"10.1016\/j.jcp.2022.111765_br0470","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1162\/089976698300017746","article-title":"Natural gradient works efficiently in learning","volume":"10","author":"Amari","year":"1998","journal-title":"Neural Comput."},{"year":"2000","series-title":"Methods of Information Geometry","author":"Amari","key":"10.1016\/j.jcp.2022.111765_br0480"},{"key":"10.1016\/j.jcp.2022.111765_br0490","doi-asserted-by":"crossref","first-page":"269","DOI":"10.22331\/q-2020-05-25-269","article-title":"Quantum natural gradient","volume":"4","author":"Stokes","year":"2020","journal-title":"Quantum"},{"year":"2021","series-title":"VMCNet: Flexible, General-Purpose VMC Framework, Built on JAX","author":"Lin","key":"10.1016\/j.jcp.2022.111765_br0500"},{"year":"2018","series-title":"JAX: Composable Transformations of Python+NumPy Programs","author":"Bradbury","key":"10.1016\/j.jcp.2022.111765_br0510"},{"key":"10.1016\/j.jcp.2022.111765_br0530","doi-asserted-by":"crossref","first-page":"3649","DOI":"10.1103\/PhysRevA.47.3649","article-title":"Ground-state correlation energies for atomic ions with 3 to 18 electrons","volume":"47","author":"Chakravorty","year":"1993","journal-title":"Phys. Rev. A"},{"key":"10.1016\/j.jcp.2022.111765_br0540","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1002\/qua.560180511","article-title":"Applicability of coupled-pair theories to quasidegenerate electronic states: a model study","volume":"18","author":"Jankowski","year":"1980","journal-title":"Int. J. Quant. Chem."},{"key":"10.1016\/j.jcp.2022.111765_br0550","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1007\/BF01030009","article-title":"Fermion nodes","volume":"63","author":"Ceperley","year":"1991","journal-title":"J. Stat. Phys."},{"key":"10.1016\/j.jcp.2022.111765_br0570","doi-asserted-by":"crossref","DOI":"10.1063\/1.2354502","article-title":"An accurate analytic potential function for ground-state N2 from a direct-potential-fit analysis of spectroscopic data","volume":"125","author":"Le Roy","year":"2006","journal-title":"J. Chem. Phys."},{"key":"10.1016\/j.jcp.2022.111765_br0580","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/S0009-2614(97)01392-4","article-title":"Accurately solving the electronic Schr\u00f6dinger equation of atoms and molecules using explicitly correlated (R12-)mr-ci: the ground state potential energy curve of N2","volume":"283","author":"Gdanitz","year":"1998","journal-title":"Chem. Phys. Lett."},{"year":"2022","series-title":"Towards the ground state of molecules via diffusion Monte Carlo on neural networks","author":"Ren","key":"10.1016\/j.jcp.2022.111765_br0590"},{"year":"2022","series-title":"Discovering quantum phase transitions with fermionic neural networks","author":"Cassella","key":"10.1016\/j.jcp.2022.111765_br0600"},{"year":"1988","series-title":"Quantum Many-Particle Systems","author":"Negele","key":"10.1016\/j.jcp.2022.111765_br0610"},{"key":"10.1016\/j.jcp.2022.111765_br0620","doi-asserted-by":"crossref","DOI":"10.1002\/wcms.1340","article-title":"Pyscf: the python-based simulations of chemistry framework","volume":"8","author":"Sun","year":"2018","journal-title":"WIREs Comput. Mol. Sci."},{"key":"10.1016\/j.jcp.2022.111765_br0630","doi-asserted-by":"crossref","DOI":"10.1063\/5.0006074","article-title":"Recent developments in the pyscf program package","volume":"153","author":"Sun","year":"2020","journal-title":"J. Chem. Phys."},{"key":"10.1016\/j.jcp.2022.111765_br0640","article-title":"Fourier Analysis, Self-Adjointness","volume":"vol. 2","author":"Reed","year":"1975"},{"key":"10.1016\/j.jcp.2022.111765_br0650","doi-asserted-by":"crossref","first-page":"2865","DOI":"10.1063\/1.465195","article-title":"A diffusion Monte Carlo algorithm with very small time-step errors","volume":"99","author":"Umrigar","year":"1993","journal-title":"J. Chem. Phys."}],"container-title":["Journal of Computational Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0021999122008282?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0021999122008282?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T18:16:25Z","timestamp":1701368185000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0021999122008282"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2]]},"references-count":62,"alternative-id":["S0021999122008282"],"URL":"https:\/\/doi.org\/10.1016\/j.jcp.2022.111765","relation":{},"ISSN":["0021-9991"],"issn-type":[{"type":"print","value":"0021-9991"}],"subject":[],"published":{"date-parts":[[2023,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation","name":"articletitle","label":"Article Title"},{"value":"Journal of Computational Physics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jcp.2022.111765","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier Inc. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"111765"}}