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SUMMARY:Solving the electronic Schrödinger equation with deep learning - 
 Jan Hermann\, Freie Universität Berlin
DTSTART:20201012T153000Z
DTEND:20201012T160000Z
UID:TALK151687@talks.cam.ac.uk
CONTACT:Bingqing Cheng
DESCRIPTION:Variational quantum Monte Carlo provides a computationally eff
 icient platform for arbitrarily accurate solutions of the electronic Schr
 ödinger equation\, but until recently the accuracy has been limited by th
 e expressiveness of the available wave function ansatzes [1]. In this talk
 \, I will present our deep-learning ansatz PauliNet [2]\, which takes adva
 ntage of deep neural networks as universal approximators to represent elec
 tronic wave functions with high fidelity. PauliNet uses a baseline HF solu
 tion and deep Jastrow factor and backflow transformation\, and reaches sta
 te-of-the-art accuracy for systems ranging from diatomic molecules\, to st
 rongly correlated H₁₀\, to cyclobutadiene (28 electrons). I will also 
 discuss the similarities and differences of PauliNet to the FermiNet ansat
 z\, which is another deep-learning ansatz [3].\n\n1. Foulkes\, W. M. C.\, 
 Mitas\, L.\, Needs\, R. J. & Rajagopal\, G. Rev. Mod. Phys. 73\, 33–83 (
 2001). https://doi.org/10.1103/RevModPhys.73.33\n\n2. Hermann\, J.\, Schä
 tzle\, Z. & Noé\, F. Nat. Chem. (2020). https://doi.org/10.1038/s41557-02
 0-0544-y\n\n3. Pfau\, D.\, Spencer\, J. S.\, Matthews\, A. G. de G. & Foul
 kes\, W. M. C. Phys. Rev. Research 2\, 033429 (2020). https://doi.org/10.1
 103/PhysRevResearch.2.033429
LOCATION:virtual ZOOM meeting ID: 263 591 6003\, https://zoom.us/j/2635916
 003
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