5 papers
Shadow Molecular Dynamics for General Nonlinear Flexible-Charge Models
Jos Thomas, Anthony P. Baldo, Rae A. Corrigan Grove +10
Machine-learned interatomic potentials have greatly expanded the reach of molecular dynamics simulations, but complex chemical environments often require explicit treatment of long…
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…
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…
PySEQM 2.0: Accelerated Semiempirical Excited State Calculations on Graphical Processing Units
Vishikh Athavale, Nikita Fedik, William Colglazier +3
We report the implementation of electronic excited states for semi-empirical quantum chemical methods at the configuration interaction singles (CIS) and time-dependent Hartree-Fock…
Semi-Empirical Shadow Molecular Dynamics: A PyTorch implementation
Maksim Kulichenko, Kipton Barros, Nicholas Lubbers +5
Extended Lagrangian Born-Oppenheimer molecular dynamics (XL-BOMD) in its most recent shadow potential energy version has been implemented in the semiempirical PyTorch-based softwar…