7 citations · 13 across the 2 of their papers we have counts for
5 papers
Quantum perturbation theory using Tensor cores and a deep neural network
Joshua Finkelstein, Emanuel H. Rubensson, Susan M. Mniszewski +2
Time-independent quantum response calculations are performed using Tensor cores. This is achieved by mapping density matrix perturbation theory onto the computational structure of…
Bringing discrete-time Langevin splitting methods into agreement with thermodynamics
Joshua Finkelstein, Chungho Cheng, Giacomo Fiorin +2
In light of the recently published complete set of statistically correct Gronbech-Jensen (GJ) methods for discrete-time thermodynamics, we revise a differential operator splitting…
Quantum-based Molecular Dynamics Simulations Using Tensor Cores
Joshua Finkelstein, Justin S. Smith, Susan M. Mniszewski +4
Tensor cores, along with tensor processing units, represent a new form of hardware acceleration specifically designed for deep neural network calculations in artificial intelligenc…
Mixed Precision Fermi-Operator Expansion on Tensor Cores From a Machine Learning Perspective
Joshua Finkelstein, Justin Smith, Susan M. Mniszewski +4
We present a second-order recursive Fermi-operator expansion scheme using mixed precision floating point operations to perform electronic structure calculations using tensor core u…
The Challenge of Stochastic Størmer-Verlet Thermostats Generating Correct Statistics
Joshua Finkelstein, Chungho Cheng, Giacomo Fiorin +2
In light of the recently developed complete GJ set of single random variable stochastic, discrete-time Størmer-Verlet algorithms for statistically accurate simulations of Langevin…