560 citations · 3.5k across the 52 of their papers we have counts for
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Streamlining Analysis and Design of Two-Dimensional Electronic Spectroscopy using Machine Learning
Nicholas I. Hausman, Joseph Kelly, Michael S. Chen +5
Two-dimensional electronic spectroscopy (2DES) offers unique insights into the coupling between electronic and nuclear motion and dynamics, making it a key technique in diverse fie…
Path Integral Methods in Atomistic Modelling: An Introduction
Michele Ceriotti, David E. Manolopoulos, Thomas E. Markland +1
This book provides an introduction to path integral methods and their application to modeling atomistic processes. The book covers both the foundational theory and recently develop…
Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations
Peter Eastman, Evan Pretti, Thomas E. Markland
The rapid development of pretrained Machine Learning Interatomic Potentials (MLIPs) that cover a wide range of molecular species has made it challenging to select the best model fo…
Pushing the limits of one-dimensional NMR spectroscopy for automated structure elucidation using artificial intelligence
Frank Hu, Jonathan M. Tubb, Dimitris Argyropoulos +5
One-dimensional NMR spectroscopy is one of the most widely used techniques for the characterization of organic compounds and natural products. For molecules with up to 36 non-hydro…
Two-dimensional electronic spectra from trajectory-based dynamics: pure-state Ehrenfest, spin-mapping, and mean classical path approaches
Annina Z. Lieberherr, Joseph Kelly, Johan E. Runeson +2
Two-dimensional electronic spectroscopy (2DES) provides a detailed picture of electronically nonadiabatic dynamics that can be interpreted with the aid of simulations. Here, we dev…
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…
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…
Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning
Peter Eastman, Benjamin P. Pritchard, John D. Chodera +1
We describe version 2 of the SPICE dataset, a collection of quantum chemistry calculations for training machine learning potentials. It expands on the original dataset by adding mu…
Enhancing Protein-Ligand Binding Affinity Predictions using Neural Network Potentials
Francesc Sabanes Zariquiey, Raimondas Galvelis, Emilio Gallicchio +3
This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural ne…
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials
Peter Eastman, Raimondas Galvelis, Raúl P. Peláez +22
Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use…
Developing machine-learned potentials to simultaneously capture the dynamics of excess protons and hydroxide ions in classical and path integral simulations
Austin O. Atsango, Tobias Morawietz, Ondrej Marsalek +1
The transport of excess protons and hydroxide ions in water underlies numerous important chemical and biological processes. Accurately simulating the associated transport mechanism…
Elucidating the role of hydrogen bonding in the optical spectroscopy of the solvated green fluorescent protein chromophore: using machine learning to establish the importance of high-level electronic structure
Michael S. Chen, Yuezhi Mao, Andrew Snider +5
Hydrogen bonding interactions with chromophores in chemical and biological environments play a key role in determining their electronic absorption and relaxation processes, which a…
Electron transfer at electrode interfaces via a straightforward quasiclassical fermionic mapping approach
Kenneth A. Jung, Joseph Kelly, Thomas E. Markland
Electron transfer at electrode interfaces to molecules in solution or at the electrode surface plays a vital role in numerous technological processes. However, treating these proce…
A derivation of the conditions under which bosonic operators exactly capture fermionic structure and dynamics
Andrés Montoya-Castillo, Thomas E. Markland
The dynamics of many-body fermionic systems are important in problems ranging from catalytic reactions at electrochemical surfaces, to transport through nanojunctions, and offer a…
An accurate and efficient Ehrenfest dynamics approach for calculating linear and nonlinear electronic spectra
Austin O. Atsango, Andrés Montoya-Castillo, Thomas E. Markland
Linear and nonlinear electronic spectra provide an important tool to probe the absorption and transfer of electronic energy. Here we introduce a pure state Ehrenfest approach to ob…
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…
SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials
Peter Eastman, Pavan Kumar Behara, David L. Dotson +9
Machine learning potentials are an important tool for molecular simulation, but their development is held back by a shortage of high quality datasets to train them on. We describe…
Simulating nuclear and electronic quantum effects in enzymes
Lu Wang, Christine M. Isborn, Thomas E. Markland
An accurate treatment of the structures and dynamics that lead to enhanced chemical reactivity in enzymes requires explicit treatment of both electronic and nuclear quantum effects…
2D spectroscopies from condensed phase dynamics: Accessing third-order response properties from equilibrium multi-time correlation functions
Kenneth A. Jung, Thomas E. Markland
The third-order response lies at the heart of simulating and interpreting nonlinear spectroscopies ranging from two dimensional infrared (2D-IR) to 2D electronic (2D-ES), and 2D su…
Optically induced anisotropy in time-resolved scattering: Imaging molecular scale structure and dynamics in disordered media with experiment and theory
Andrés Montoya-Castillo, Michael S. Chen, Sumana L. Raj +6
Time-resolved scattering experiments enable imaging of materials at the molecular scale with femtosecond time resolution. However, in disordered media they provide access to just o…
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…
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…
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…
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…