activity
20102022
most citedCompeting quantum effects in the dynamics of a flexible water model

560 citations · 776 across the 11 of their papers we have counts for

collaborators

17 papers

physics.chem-ph20222 cited

Machine learning potentials from transfer learning of periodic correlated electronic structure methods: Application to liquid water with AFQMC, CCSD, and CCSD(T)

Michael S. Chen, Joonho Lee, Hong-Zhou Ye +3

Obtaining the atomistic structure and dynamics of disordered condensed phase systems from first principles remains one of the forefront challenges of chemical theory. Here we explo…

physics.chem-ph20217 cited

Characterizing and contrasting structural proton transport mechanisms in azole hydrogen bond networks using ab initio molecular dynamics

Austin O. Atsango, Mark E. Tuckerman, Thomas E. Markland

Imidazole and 1,2,3-triazole are promising hydrogen-bonded heterocycles that conduct protons via a structural mechanism and whose derivatives are present in systems ranging from bi…

physics.chem-ph202130 cited

Elucidating the proton transport pathways in liquid imidazole with first-principles molecular dynamics

Zhuoran Long, Austin O. Atsango, Joseph A. Napoli +2

Imidazole is a promising anhydrous proton conductor with a high conductivity comparable to that of water at a similar temperature relative to its melting point. Previous theoretica…

cond-mat.mtrl-sci202129 cited

AENET-LAMMPS and AENET-TINKER: Interfaces for Accurate and Efficient Molecular Dynamics Simulations with Machine Learning Potentials

Michael S. Chen, Tobias Morawietz, Hideki Mori +2

Machine learning potentials (MLPs) trained on data from quantum-mechanics based first-principles methods can approach the accuracy of the reference method at a fraction of the comp…

physics.chem-ph202020 cited

Excited state diabatization on the cheap using DFT: Photoinduced electron and hole transfer

Yuezhi Mao, Andres Montoya-Castillo, Thomas E. Markland

Excited state electron and hole transfer underpin fundamental steps in processes such as exciton dissociation at photovoltaic heterojunctions, photoinduced charge transfer at elect…

physics.chem-ph2020

Exploiting machine learning to efficiently predict multidimensional optical spectra in complex environments

Michael S. Chen, Tim J. Zuehlsdorff, Tobias Morawietz +2

The excited state dynamics of chromophores in complex environments determine a range of vital biological and energy capture processes. Time-resolved, multidimensional optical spect…