130 citations · 297 across the 22 of their papers we have counts for
5 papers · 1 filter
Global properties of the energy landscape: a testing and training arena for machine learned potentials
Vlad Cărare, Fabian L. Thiemann, Joe Morrow +3
Machine learning interatomic potentials (MLIPs) have achieved remarkable accuracy on standard benchmarks, yet their ability to reproduce molecular kinetics -- critical for reaction…
Hessian QM9: A quantum chemistry database of molecular Hessians in implicit solvents
Nicholas J. Williams, Lara Kabalan, Ljiljana Stojanovic +2
A significant challenge in computational chemistry is developing approximations that accelerate \emph{ab initio} methods while preserving accuracy. Machine learning interatomic pot…
Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks
Clyde Fare, Lukas Turcani, Edward O. Pyzer-Knapp
Chemical representations derived from deep learning are emerging as a powerful tool in areas such as drug discovery and materials innovation. Currently, this methodology has three…
Machine Learning Exciton Dynamics
Florian Häse, Stéphanie Valleau, Edward Pyzer-Knapp +1
Obtaining the exciton dynamics of large photosynthetic complexes by using mixed quantum mechanics/molecular mechanics (QM/MM) is computationally demanding. We propose a machine lea…
A Bayesian Approach to Calibrating High-Throughput Virtual Screening Results and Application to Organic Photovoltaic Materials
Edward O. Pyzer-Knapp, Gregor N. Simm, Alan Aspuru-Guzik
A novel approach for calibrating quantum-chemical properties determined as part of a high-throughput virtual screen to experimental analogs is presented. Information on the molecul…