18 citations · 34 across the 8 of their papers we have counts for
10 papers
Susceptibility Formulation of Density Matrix Perturbation Theory
Anders M. N. Niklasson, Adela Habib, Joshua Finkelstein +1
Density matrix perturbation theory based on recursive Fermi-operator expansions provides a computationally efficient framework for time-independent response calculations in quantum…
Efficient Mixed-Precision Matrix Factorization of the Inverse Overlap Matrix in Electronic Structure Calculations with AI-Hardware and GPUs
Adela Habib, Joshua Finkelstein, Anders M. N. Niklasson
In recent years, a new kind of accelerated hardware has gained popularity in the Artificial Intelligence (AI) and Machine Learning (ML) communities which enables extremely high-per…
Hybrid programming-model strategies for GPU offloading of electronic structure calculation kernels
Jean-Luc Fattebert, Christian F. A. Negre, Joshua Finkelstein +6
To address the challenge of performance portability, and facilitate the implementation of electronic structure solvers, we developed the Basic Matrix Library (BML) and Parallel, Ra…
A fast, dense Chebyshev solver for electronic structure on GPUs
Joshua Finkelstein, Christian F. A. Negre, Jean-Luc Fattebert
Matrix diagonalization is almost always involved in computing the density matrix needed in quantum chemistry calculations. In the case of modest matrix sizes ( 5000), per…
A Methodology to Generate Crystal-based Molecular Structures for Atomistic Simulations
Christian F. A. Negre, Andrew Alvarado, Himanshu Singh +3
We propose a systematic method to construct crystal-based molecular structures often needed as input for computational chemistry studies. These structures include crystal ``slabs"…
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…