Comments (2)
Yes, for Qeq model, DMFF currently only supports rigorously adiabatic solutions (such as matrix inversion or root finding algorithms). It does not provide an extended-Lagrangian integrator yet. So you will have to implement the integrator yourself, or interface DMFF with a MD simulation package with this feature. However, DMFF can provide you with all the gradients you need for this task (like dU/dQ, which you would need in the integrator, can be computed trivially using DMFF).
And as we discussed in the last time, the speed could be a bigger concern if you are going to use DMFF in serious large scale simulations. When we interface DMFF with other MD packages, the data trafficing issue is slowing down the entire simulation. Things could be potentially better if we using JAX to write our own integrator, which is something we are probably going to do in future, but right now it is not the top priority of the development team (as DMFF was primarily designed for model optimization...).
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Yes, for Qeq model, DMFF currently only supports rigorously adiabatic solutions (such as matrix inversion or root finding algorithms). It does not provide an extended-Lagrangian integrator yet. So you will have to implement the integrator yourself, or interface DMFF with a MD simulation package with this feature. However, DMFF can provide you with all the gradients you need for this task (like dU/dQ, which you would need in the integrator, can be computed trivially using DMFF).
And as we discussed in the last time, the speed could be a bigger concern if you are going to use DMFF in serious large scale simulations. When we interface DMFF with other MD packages, the data trafficing issue is slowing down the entire simulation. Things could be potentially better if we using JAX to write our own integrator, which is something we are probably going to do in future, but right now it is not the top priority of the development team (as DMFF was primarily designed for model optimization...).
Thanks for Prof. Yu's reply and suggestion! For my case, it is a better choice to inplement the model directly in MD engine without the data trafficing between the software.
from dmff.
Related Issues (20)
- [Feature Request] Request a new feature for development of reactive force field HOT 3
- [BUG] spatial.py shape error
- Question about gradient HOT 7
- [Feature Request] Change the API of polarizable potential (ADMP and Qeq) functions HOT 1
- [Feature Request] Change the default unit of the ADMP frontend
- [BUG] Out of Memory Issue During Neighbor List Generation HOT 2
- [BUG] Matching energies to reference after dynamics HOT 2
- [Feature Request] Install dependencies automatically and publish to PyPI HOT 3
- [Feature Request] Support for machine learning force field in OpenMM DMFF plugin HOT 1
- Installation problem
- Op type not registered 'XlaSharding' in binary running HOT 1
- [Feature Request] Refactor the cpp interface of the saved DMFF jax model with MD engine
- [BUG] LJ switch function
- [Feature Request] Support Virtual Site in DMFF frontend HOT 1
- [Feature Request] MD Engine Support (LAMMPS) for trained model with DMFF
- [Feature Request] Support parameter optimization for dynamic properties based on trajectory HOT 2
- [Feature Request] QEQ modelโs parameterization for conductive electrodes with DMFF HOT 1
- [Feature Request] Workflow to fit relative protein-ligand binding free energy data
- [Feature Request] Efficient neighborlist construction/usage plugin for nonbonded force calculation HOT 1
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