B. Clark

发表

Simulating 2+1D Lattice Quantum Electrodynamics at Finite Density with Neural Flow Wavefunctions

Di Luo, Zhuo Chen, Kaiwen Hu, 2022, ArXiv.

Gauge Equivariant Neural Networks for 2+1D U(1) Gauge Theory Simulations in Hamiltonian Formulation

J. Stokes, Di Luo, Shunyue Yuan, 2022, ArXiv.

Detecting Vacancy-Induced Strain Field Oscillations via Deep Learning

Pinshane Y. Huang, N. Sobh, Sangmin Kang, 2020, Microscopy and Microanalysis.

Deep Learning Enabled Strain Mapping of Single-Atom Defects in 2D Transition Metal Dichalcogenides with Sub-picometer Precision.

Pinshane Y. Huang, N. Sobh, Sangmin Kang, 2020, Nano letters.

Probabilistic simulation of quantum circuits using a deep-learning architecture

J. Carrasquilla, L. Aolita, A. Milsted, 2021, Physical Review A.

Gauge-invariant and anyonic-symmetric autoregressive neural network for quantum lattice models

Zhizhen Zhao, Di Luo, V. M. Hur, 2021, Physical Review Research.

Using CycleGANs to Generate Realistic STEM Images for Machine Learning

Pinshane Y. Huang, Chia-Hao Lee, Abid Khan, 2023, ArXiv.

Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy images

Pinshane Y. Huang, Chia-Hao Lee, Abid Khan, 2023, npj Computational Materials.

9:20am IE-MoM5 Understanding Dislocation Dynamics and the link

S. Kodambaka, G. Liu, B. Clark, 2007 .

Deep Learning Enabled Measurements of Single-Atom Defects in 2D Transition Metal Dichalcogenides with Sub-Picometer Precision

Pinshane Y. Huang, Sangmin Kang, Di Luo, 2019, Microscopy and Microanalysis.