6 papers
Accelerating Multicanonical Sampling with Irreversibility
Thomas Vogel, Ying Wai Li
Flat-histogram Monte Carlo simulations are well-established, robust methods to perform random walks in a physical observable or parameter space, making them suitable for finding gr…
Machine learning topological defect formation
Fumika Suzuki, Ying Wai Li, Wojciech H. Zurek
According to the Kibble-Zurek mechanism (KZM), the density of topological defects created during a second-order phase transition is determined by the correlation length at the free…
Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials
Sakib Matin, Emily Shinkle, Yulia Pimonova +5
The quality of machine learning interatomic potentials (MLIPs) strongly depends on the quantity of training data as well as the quantum chemistry (QC) level of theory used. Dataset…
Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials
Sakib Matin, Alice E. A. Allen, Emily Shinkle +9
Machine learning interatomic potentials (MLIPs) are revolutionizing the field of molecular dynamics (MD) simulations. Recent MLIPs have tended towards more complex architectures tr…
Quantum ensemble learning with a programmable superconducting processor
Jiachen Chen, Yaozu Wu, Zhen Yang +30
Quantum machine learning is among the most exciting potential applications of quantum computing. However, the vulnerability of quantum information to environmental noises and the c…
Sunny.jl: A Julia Package for Spin Dynamics
David Dahlbom, Hao Zhang, Cole Miles +16
Sunny is a Julia package designed to serve the needs of the quantum magnetism community. It supports the specification of a very broad class of spin models and a diverse suite of n…