29 citations · 34 across the 4 of their papers we have counts for
5 papers · 1 filter
Two-dimensional electronic spectroscopy in the condensed phase using equivariant transformer accelerated molecular dynamics simulations
Joseph Kelly, Frank Hu, Arianna Damiani +10
Two-dimensional electronic spectroscopy (2DES) provides rich information about how the electronic states of molecules, proteins, and solid-state materials interact with each other…
On the design space between molecular mechanics and machine learning force fields
Yuanqing Wang, Kenichiro Takaba, Michael S. Chen +14
A force field as accurate as quantum mechanics (QM) and as fast as molecular mechanics (MM), with which one can simulate a biomolecular system efficiently enough and meaningfully e…
Accurate and efficient structure elucidation from routine one-dimensional NMR spectra using multitask machine learning
Frank Hu, Michael S. Chen, Grant M. Rotskoff +2
Rapid determination of molecular structures can greatly accelerate workflows across many chemical disciplines. However, elucidating structure using only one-dimensional (1D) NMR sp…
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…
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…