Comments (3)
Maybe conda recipes will be necessary in the future.
The most commonly used, and perhaps the most user-friendly, method of installing OpenMM is through conda. (see http://docs.openmm.org/latest/userguide/application/01_getting_started.html#installing-openmm).
Considering that OpenMM is a mandatory dependency for dmff, it is reasonable to assume that most users already have conda installed. Also, the dmff dependencies can be primarily configured within conda.
In the future, dmff is expected to include binary files for OpenMM plugins, as developing with conda in https://github.com/deepmodeling/DMFF/tree/devel/backend. Given this, I recommend using conda recipes directly for distributing dmff instead of PyPI.
In addition, it is also worth considering whether a separate distribution of OpenMMDMFFPlugin(if named as such) is necessary.
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When building a conda recipe, one still needs to use pip install
to install the package and may use pip check
to check whether the dependencies are correctly installed. Only writing dependencies to the conda recipe is unsafe.
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xref for mdtraj: mdtraj/mdtraj#1794
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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] 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
- update the charge HOT 2
- [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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