.png){kind=icon}
Return
Robust and Automated Force Field Parameterization Using Validation Sets and Active Learning
.png){kind=icon}
PRE
AI
DOI：10.1021/acs.jctc.5c01280.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Molecular mechanics force fields enable atomistic simulations of complex systems that are too large for a quantum mechanical treatment. Simulation accuracy depends on the parameters employed in the force field. Every new molecule must have parameters generated for it, either by using a general force field or fitting a custom parameter set for that system. While fitting custom parameter sets can provide superior accuracy compared to a general force field, the process of single-molecule force field fitting is often tedious, expensive, and bespoke. We present an automated and iterative procedure for fitting single-molecule force fields. This program optimizes the parameters with respect to a data set of quantum mechanical (QM) calculations, runs dynamics with the new parameters to sample new conformations, computes QM energies and forces on those conformations, adds them to the data set, and returns to the parameter optimization step. In contrast to previous attempts at iterative optimization, we employ a validation set to determine convergence. Using a validation set circumvents problems with parameter convergence and flags when overfitting occurs. As an example, we find that Boltzmann sampling at 400 K is sufficient to fit a force field for a trialanine peptide, a system with a rugged potential energy surface. Last, we demonstrate the efficiency of the method by fitting a custom force field for each molecule in a library of 31 photosynthesis cofactors.
Journal
IF:
5.5
Papers:
1.1W
Citations:
5.4W
Organization
Cited Papers
.png){kind=icon}
ShareSaveGaussian Accelerated Molecular Dynamics: Unconstrained Enhanced Sampling and Free Energy Calculation
OAAI
OAAIShareSaveShareSave
View more