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SUMMARY:Frank Noe (FU Berlin)
DTSTART:20200701T160000Z
DTEND:20200701T162500Z
DTSTAMP:20260423T035919Z
UID:SciDL/3
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/SciDL/3/">Pa
 uliNet: Deep neural network solution of the electronic Schrödinger Equati
 on</a>\nby Frank Noe (FU Berlin) as part of Workshop on Scientific-Driven 
 Deep Learning (SciDL)\n\n\nAbstract\nThe electronic Schrödinger equation 
 describes fundamental properties of molecules and materials\, but can only
  be solved analytically for the hydrogen atom. The numerically exact full 
 configuration-interaction method is exponentially expensive in the number 
 of electrons. Quantum Monte Carlo is a possible way out: it scales well to
  large molecules\, can be parallelized\, and its accuracy has\, as yet\, o
 nly been limited by the flexibility of the used wave function ansatz. Here
  we propose PauliNet\, a deep-learning wave function ansatz that achieves 
 nearly exact solutions of the electronic Schrödinger equation. PauliNet h
 as a multireference Hartree-Fock solution built in as a baseline\, incorpo
 rates the physics of valid wave functions\, and is trained using variation
 al quantum Monte Carlo (VMC). PauliNet outperforms comparable state-of-the
 -art VMC ansatzes for atoms\, diatomic molecules and a strongly-correlated
  hydrogen chain by a margin and is yet computationally efficient. We antic
 ipate that thanks to the favourable scaling with system size\, this method
  may become a new leading method for highly accurate electronic-strucutre 
 calculations on medium-sized molecular systems.\n
LOCATION:https://researchseminars.org/talk/SciDL/3/
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