84 citations · 381 across the 30 of their papers we have counts for
34 papers
GPU-Accelerated Charge-Equilibration for Shadow Molecular Dynamics in Python
Mehmet Cagri Kaymak, Nicholas Lubbers, Christian F. A. Negre +2
With recent advancements in machine learning for interatomic potentials, Python has become the go-to programming language for exploring new ideas. While machine-learning potentials…
Modeling Reactions on the Solid-Liquid Interface With Next Generation Extended Lagrangian Quantum-Based Molecular Dynamics
Rae A. Corrigan Grove, Kevin G. Kleiner, Joshua Finkelstein +5
We present a series of simulations of the oxygen reduction reaction (ORR) using a novel framework for atomistic simulations of surface catalysis under electrochemical bias. The fra…
Towards Faster Matrix Diagonalization with Graph Isomorphism Networks and the AlphaZero Framework
Geigh Zollicoffer, Kshitij Bhatta, Manish Bhattarai +4
In this paper, we introduce innovative approaches for accelerating the Jacobi method for matrix diagonalization, specifically through the formulation of large matrix diagonalizatio…
Accelerating Matrix Diagonalization through Decision Transformers with Epsilon-Greedy Optimization
Kshitij Bhatta, Geigh Zollicoffer, Manish Bhattarai +4
This paper introduces a novel framework for matrix diagonalization, recasting it as a sequential decision-making problem and applying the power of Decision Transformers (DTs). Our…
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…
Matrix Diagonalization as a Board Game: Teaching an Eigensolver the Fastest Path to Solution
Phil Romero, Manish Bhattarai, Christian F. A. Negre +2
Matrix diagonalization is at the cornerstone of numerous fields of scientific computing. Diagonalizing a matrix to solve an eigenvalue problem requires a sequential path of iterati…
Extended Lagrangian Born-Oppenheimer Molecular Dynamics with DFT+U
Yu Zhang, Marc J. Cawkwell, Christian F. A. Negre +2
Extended Lagrangian Born-Oppenheimer molecular dynamics (XL-BOMD) [Phys. Rev. Lett. vol. 100, 123004 (2008)] is combined with Kohn-Sham density functional theory (DFT) using a DFT+…
Spin-Polarized Extended Lagrangian Born-Oppenheimer Molecular Dynamics
Yu Zhang, Marc J. Cawkwell, Christian F. A. Negre +2
We present a generalization of Extended Lagrangian Born-Oppenheimer molecular dynamics [Phys. Rev. Lett. vol. 100, 123004 (2008); Eur. Phys. J. B vol. 94, 164 (2021)] that also inc…
Shadow molecular dynamics and atomic cluster expansions for flexible charge models
James Goff, Yu Zhang, Christian F. A. Negre +2
A shadow molecular dynamics scheme for flexible charge models is presented, where the shadow Born-Oppenheimer potential is derived from a coarse-grained approximation of range-sepa…
Shadow Energy Functionals and Potentials in Born-Oppenheimer Molecular Dynamics
Anders M. N. Niklasson, Christian F. A. Negre
In Born-Oppenheimer molecular dynamics (BOMD) simulations based on density functional theory (DFT), the potential energy and the interatomic forces are calculated from an electroni…
Graph-based Quantum Response Theory and Shadow Born-Oppenheimer Molecular Dynamics
Christian F. A. Negre, Michael E. Wall, Anders M. N. Niklasson
Graph-based linear scaling electronic structure theory for quantum-mechanical molecular dynamics simulations is adapted to the most recent shadow potential formulations of extended…
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 Computing for a Profusion of Postman Problem Variants
Joel E. Pion, Christian F. A. Negre, Susan M. Mniszewski
In this paper we study the viability of solving the Chinese Postman Problem, a graph routing optimization problem, and many of its variants on a quantum annealing device. Routing p…
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…
QUBO-based density matrix electronic structure method
Christian F. A. Negre, Alejandro Lopez-Bezanilla, Yu Zhang +4
Density matrix electronic structure theory is used in many quantum chemistry methods to "alleviate" the computational cost that arises from directly using wave functions. Although…
Enabling particle applications for exascale computing platforms
Susan M Mniszewski, James Belak, Jean-Luc Fattebert +21
The Exascale Computing Project (ECP) is invested in co-design to assure that key applications are ready for exascale computing. Within ECP, the Co-design Center for Particle Applic…
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…
Computing molecular excited states on a D-Wave quantum annealer
Alexander Teplukhin, Brian K. Kendrick, Susan M. Mniszewski +6
The possibility of using quantum computers for electronic structure calculations has opened up a promising avenue for computational chemistry. Towards this direction, numerous algo…
Variational Quantum Eigensolver with Reduced Circuit Complexity
Yu Zhang, Lukasz Cincio, Christian F. A. Negre +6
The variational quantum eigensolver (VQE) is one of the most promising algorithms to find eigenvalues and eigenvectors of a given Hamiltonian on noisy intermediate-scale quantum (N…
A QUBO formulation for top- eigencentrality nodes
Prosper D. Akrobotu, Tamsin E. James, Christian F. A. Negre +1
The efficient calculation of the centrality or "hierarchy" of nodes in a network has gained great relevance in recent years due to the generation of large amounts of data. The eige…
A QUBO Algorithm to Compute Eigenvectors of Symmetric Matrices
Benjamin Krakoff, Susan M. Mniszewski, Christian F. A. Negre
We describe an algorithm to compute the extremal eigenvalues and corresponding eigenvectors of a symmetric matrix by solving a sequence of Quadratic Binary Optimization problems. T…
Performance Optimizations of Recursive Electronic Structure Solvers targeting Multi-Core Architectures (LA-UR-20-26665)
Adetokunbo A. Adedoyin, Christian F. A. Negre, Jamaludin Mohd-Yusof +6
As we rapidly approach the frontiers of ultra large computing resources, software optimization is becoming of paramount interest to scientific application developers interested in…
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…