872 citations · 2.9k across the 35 of their papers we have counts for
6 papers · 1 filter
Machine learning potentials with Iterative Boltzmann Inversion: training to experiment
Sakib Matin, Alice Allen, Justin S. Smith +7
Methodologies for training machine learning potentials (MLPs) to quantum-mechanical simulation data have recently seen tremendous progress. Experimental data has a very different c…
Learning Together: Towards foundational models for machine learning interatomic potentials with meta-learning
Alice E. A. Allen, Nicholas Lubbers, Sakib Matin +4
The development of machine learning models has led to an abundance of datasets containing quantum mechanical (QM) calculations for molecular and material systems. However, traditio…
Machine Learning Framework for Modeling Exciton-Polaritons in Molecular Materials
Xinyang Li, Nicholas Lubbers, Sergei Tretiak +2
A light-matter hybrid quasiparticle, called a polariton, is formed when molecules are strongly coupled to an optical cavity. Recent experiments have shown that polariton chemistry…
Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces
Javier E Santos, Zachary R. Fox, Nicholas Lubbers +1
Typical generative diffusion models rely on a Gaussian diffusion process for training the backward transformations, which can then be used to generate samples from Gaussian noise.…
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…
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…