A5049503956- Machine Learning in Materials Science
- Advanced Chemical Physics Studies
- Quantum many-body systems
- Spectroscopy and Quantum Chemical Studies
- Neural Networks and Reservoir Computing
- Theoretical and Computational Physics
- Physics of Superconductivity and Magnetism
Freie Universität Berlin
2023
Charles University
2023
Computing accurate yet efficient approximations to the solutions of electronic Schrödinger equation has been a paramount challenge computational chemistry for decades. Quantum Monte Carlo methods are promising avenue development as their core algorithm exhibits number favorable properties: it is highly parallel and scales favorably with considered system size, an accuracy that limited only by choice wave function Ansatz. The recently introduced machine-learned parametrizations quantum...
We utilize neural network quantum states (NQS) to investigate the ground state properties of Heisenberg model on a Shastry-Sutherland lattice using variational Monte Carlo method. show that already relatively simple NQSs can be used approximate this in its different phases and regimes. first compare several types with each other small lattices benchmark their energies against exact diagonalization results. argue when precision, generality, computational costs are taken into account, good...
We utilize neural network quantum states (NQS) to investigate the ground state properties of Heisenberg model on a Shastry-Sutherland lattice using variational Monte Carlo method. show that already relatively simple NQSs can be used approximate this in its different phases and regimes. first compare several types with each other small lattices benchmark their energies against exact diagonalization results. argue when precision, generality, computational costs are taken into account, good...
Computing accurate yet efficient approximations to the solutions of electronic Schr\"odinger equation has been a paramount challenge computational chemistry for decades. Quantum Monte Carlo methods are promising avenue development as their core algorithm exhibits number favorable properties: it is highly parallel, and scales favorably with considered system size, an accuracy that limited only by choice wave function ansatz. The recently introduced machine-learned parametrizations quantum...