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Found 11 papers, 5 papers with code
Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy images
The rise of automation and machine learning (ML) in electron microscopy has the potential to revolutionize materials research through autonomous data collection and processing.
Simulating 2+1D Lattice Quantum Electrodynamics at Finite Density with Neural Flow Wavefunctions
We present a neural flow wavefunction, Gauge-Fermion FlowNet, and use it to simulate 2+1D lattice compact quantum electrodynamics with finite density dynamical fermions.
Gauge Equivariant Neural Networks for 2+1D U(1) Gauge Theory Simulations in Hamiltonian Formulation
Gauge Theory plays a crucial role in many areas in science, including high energy physics, condensed matter physics and quantum information science.
Learning ground states of quantum Hamiltonians with graph networks
In this work we use graph neural networks to define a structured variational manifold and optimize its parameters to find high quality approximations of the lowest energy solutions on a diverse set of Heisenberg Hamiltonians.
Spacetime Neural Network for High Dimensional Quantum Dynamics
We develop a spacetime neural network method with second order optimization for solving quantum dynamics from the high dimensional Schr\"{o}dinger equation.
Gauge Invariant and Anyonic Symmetric Autoregressive Neural Networks for Quantum Lattice Models
Symmetries such as gauge invariance and anyonic symmetry play a crucial role in quantum many-body physics.
Gauge equivariant neural networks for quantum lattice gauge theories
Gauge symmetries play a key role in physics appearing in areas such as quantum field theories of the fundamental particles and emergent degrees of freedom in quantum materials.
Distributed-Memory DMRG via Sparse and Dense Parallel Tensor Contractions
The Density Matrix Renormalization Group (DMRG) algorithm is a powerful tool for solving eigenvalue problems to model quantum systems.
Distributed, Parallel, and Cluster Computing Strongly Correlated Electrons Computational Physics
Numerical evidence for many-body localization in two and three dimensions
These transitions in the one-dimensional Heisenberg model and two-dimensional Bose-Hubbard model coincide well with past estimates of the critical disorder strengths in these models which further validates the evidence of MBL phenomenology in the other two and three-dimensional models we examine.
Disordered Systems and Neural Networks
Deep Learning Enabled Strain Mapping of Single-Atom Defects in 2D Transition Metal Dichalcogenides with Sub-picometer Precision
2D materials offer an ideal platform to study the strain fields induced by individual atomic defects, yet challenges associated with radiation damage have so-far limited electron microscopy methods to probe these atomic-scale strain fields.
Materials Science Mesoscale and Nanoscale Physics
Engineering Topological Models with a General-Purpose Symmetry-to-Hamiltonian Approach
We use our new approach to construct new Hamiltonians for topological phases of matter.
Strongly Correlated Electrons Quantum Physics
