
AbInitio Solution of the ManyElectron Schrödinger Equation with Deep Neural Networks
Given access to accurate solutions of the manyelectron Schrödinger equa...
09/05/2019 ∙ by David Pfau, et al. ∙ 0 ∙ shareread it

Enhancing the efficiency of quantum annealing via reinforcement: A pathintegral Monte Carlo simulation of the quantum reinforcement algorithm
The standard quantum annealing algorithm tries to approach the ground st...
12/06/2018 ∙ by A. Ramezanpour, et al. ∙ 0 ∙ shareread it

Wave solutions of GilsonPickering equation
In this work, we apply the (1/G')expansion method to produce the novel ...
07/14/2019 ∙ by Karmina Kamal Ali, et al. ∙ 0 ∙ shareread it

Rapid mixing of path integral Monte Carlo for 1D stoquastic Hamiltonians
Path integral quantum Monte Carlo (PIMC) is a method for estimating ther...
12/05/2018 ∙ by Elizabeth Crosson, et al. ∙ 0 ∙ shareread it

Symmetryadapted generation of 3d point sets for the targeted discovery of molecules
Deep learning has proven to yield fast and accurate predictions of quant...
06/02/2019 ∙ by Niklas W. A. Gebauer, et al. ∙ 0 ∙ shareread it

Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
We introduce a new molecular dataset, named Alchemy, for developing mach...
06/22/2019 ∙ by Guangyong Chen, et al. ∙ 15 ∙ shareread it

Learning to Plan Chemical Syntheses
From medicines to materials, small organic molecules are indispensable f...
08/14/2017 ∙ by Marwin H. S. Segler, et al. ∙ 0 ∙ shareread it
Deep neural network solution of the electronic Schrödinger equation
The electronic Schrödinger equation describes fundamental properties of molecules and materials, but cannot be solved exactly for larger systems than the hydrogen atom. Quantum Monte Carlo is a suitable method when highquality approximations are sought, and its accuracy is in principle limited only by the flexibility of the used wavefunction ansatz. Here we develop a deeplearning wavefunction ansatz, dubbed PauliNet, which has the HartreeFock solution built in as a baseline, incorporates the physics of valid wave functions, and is trained using variational quantum Monte Carlo (VMC). Our deeplearning method achieves higher accuracy than comparable stateoftheart VMC ansatzes for atoms, diatomic molecules and a stronglycorrelated hydrogen chain. We anticipate that this method can reveal new physical insights and provide guidance for the design of molecules and materials where highly accurate quantummechanical solutions are needed, such as in transition metals and other strongly correlated systems.
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