545 citations · 2.9k across the 55 of their papers we have counts for
56 papers
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…
Physically interpretable approximations of many-body spectral functions
Shubhang Goswami, Kipton Barros, Matthew R. Carbone
The rational function approximation provides a natural and interpretable representation of response functions such as the many-body spectral functions. We apply the Vector Fitting…
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…
Renormalized Classical Theory of Quantum Magnets
David Dahlbom, Hao Zhang, Zoha Laraib +3
We derive a renormalized classical spin (RCS) theory for quantum magnets by constraining a generalized classical theory that includes all multipolar fluctuations to a red…
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…