8 papers
Machine-learned, finite temperature Fermi-operator expansions suitable for GPUs and AI-hardware
Stanislaw Kowalski, Christian F. A. Negre, Anders M. N. Niklasson +2
We present several finite-temperature recursive Fermi-operator expansion schemes based on the second-order spectral projection (SP2) method. Our approach builds on a previous obser…
SEDACS: A Scalable Framework for Complex Chemistry Simulations
Cheng-Han Li, Joshua Finkelstein, Maksim Kulichenko +5
Graph-based linear-scaling electronic-structure theory provides a scalable framework for parallel quantum-mechanical molecular dynamics (QMD) simulations by exploiting the nearsigh…
Shadow Molecular Dynamics for Flexible Multipole Models
Rae A. Corrigan Grove, Robert Stanton, Michael E. Wall +1
Shadow molecular dynamics provide an efficient and stable atomistic simulation framework for flexible charge models with long-range electrostatic interactions. While previous imple…
Enhancing Molecular Dipole Moment Prediction with Multitask Machine Learning
William Colglazier, Nicholas Lubbers, Sergei Tretiak +2
We present a multitask machine learning strategy for improving the prediction of molecular dipole moments by simultaneously training on quantum dipole magnitudes and inexpensive Mu…
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…