Browsing by Author "Eastman, Peter"

Sort by: Order: Results:

  • NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanics
    Galvelis, Raimondas; Varela-Rial, Alejandro; Doerr, Stefan, 1987-; Fino, Roberto; Eastman, Peter; Markland, Thomas E.; Chodera, John D.; De Fabritiis, Gianni (American Chemical Society (ACS), 2023)
    Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations. However, their application is constrained by the significant computational cost arising from the vast number of ...
  • SPICE, a dataset of drug-like molecules and peptides for training machine learning potentials
    Eastman, Peter; Behara, Pavan Kumar; Dotson, David L.; Galvelis, Raimondas; Herr, John E.; Horton, Josh T.; Mao, Yuezhi; Chodera, John D.; Pritchard, Benjamin P.; Wang, Yuanqing; De Fabritiis, Gianni; Markland, Thomas E. (Nature Research, 2023)
    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 the SPICE dataset, a new quantum chemistry ...

Search DSpace

Browse

My Account

In collaboration with Compliant to Partaking