Releases: deepmodeling/DMFF
Releases · deepmodeling/DMFF
v1.0.0 Released
DMFF v1.0.0 Released!
New features:
- Implemented QEQ electrostatic potential under constant charge and constant potential modes.
- Refactored the API layer, enhancing frontend robustness.
- Support calculating electrostatic potential in non-periodic systems for point charge, BCC charge, QEQ model, and multipole model.
- Added dmff.api.DMFFTopology class for storing topological information.
- Added dmff.api.ParamSet class, supporting Pytree data structures for improved management of force field parameters.
- Improved computation efficiency of PME kernel on GPU.
- Implemented backend/save_dmff2tf.py, which stores DMFF potential as a TensorFlow module for use in C/C++ API calls.
- Developed an OpenMM plugin, enabling the use of DMFF potential within OpenMM.
v0.2.0 Released
DMFF v0.2.0 Released!
New features:
- Support statistical property optimization using differentiable MBAR estimator
- Support NBFix in Lennard-Jones potential
- Support assigning parameters based on SMIRKS-pattern
- Support bond charge correction (BCC) and virtual site
- Support building neighborlist with
freud
package
v0.1.1 Released
DMFF v0.1.1 Released!
- Fix bugs in parsing multiple force field files
- Fix bugs in LJ force
- Refine examples and installation code
v0.1.0 Released!
DMFF v0.1.0 released!
- Support differentiable calculation of classical, multiple polarizable and subgragh neural network force fields
- Refined documents and examples
- CI/CD workflows