12 citations · 20 across the 4 of their papers we have counts for
6 papers
Bridging Molecular Scales with Implicit Score Matching for Bottom-Up Coarse Graining
Patrick G. Sahrmann, Nicholas Lubbers, Benjamin T. Nebgen +4
Molecular dynamics simulations provide a computational microscope for atomic scale processes but remain restricted to relatively small spatial and temporal scales. Coarse-grained m…
Reactive Chemistry at Unrestricted Coupled Cluster Level: High-throughput Calculations for Training Machine Learning Potentials
Alice E. A. Allen, Rui Li, Sakib Matin +8
Accurately modeling chemical reactions at the atomistic level requires high-level electronic structure theory due to the presence of unpaired electrons and the need to properly des…
Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials
Sakib Matin, Alice E. A. Allen, Emily Shinkle +9
Machine learning interatomic potentials (MLIPs) are revolutionizing the field of molecular dynamics (MD) simulations. Recent MLIPs have tended towards more complex architectures tr…
A Diffusion Quantum Monte Carlo Approach to the Polaritonic Ground State
Braden M. Weight, Sergei Tretiak, Yu Zhang
Making and using polaritonic states (i.e., hybrid electron-photon states) for chemical applications have recently become one of the most prominent and active fields that connects t…
Sympathetic Mechanism for Vibrational Condensation Enabled by Polariton Optomechanical Interaction
Vladislav Yu. Shishkov, Evgeny S. Andrianov, Sergei Tretiak +2
We demonstrate a macro-coherent regime in exciton-polariton systems, where nonequilibrium polariton Bose--Einstein condensation coexists with macroscopically occupied vibrational s…
Machine Learning Models Capture Plasmon Dynamics in Ag Nanoparticles
Adela Habib, Nicholas Lubbers, Sergei Tretiak +1
Highly energetic electron-hole pairs (hot carriers) formed from plasmon decay in metallic nanostructures promise sustainable pathways for energy-harvesting devices. However, effici…