3 papers
cond-mat.str-el2026
Neural network backflow for ab-initio solid calculations
An-Jun Liu, Bryan K. Clark
Accurately simulating extended periodic systems is a central challenge in condensed matter physics. Neural quantum states (NQS) offer expressive wavefunctions for this task but fac…
physics.chem-ph2025
Efficient optimization of neural network backflow for ab-initio quantum chemistry
An-Jun Liu, Bryan K. Clark
The ground state of second-quantized quantum chemistry Hamiltonians is key to determining molecular properties. Neural quantum states (NQS) offer flexible and expressive wavefuncti…
physics.chem-ph2024
Neural network backflow for ab-initio quantum chemistry
An-Jun Liu, Bryan K. Clark
The ground state of second-quantized quantum chemistry Hamiltonians provides access to an important set of chemical properties. Wavefunctions based on ML architectures have shown p…