- Fix installing ptemcee
- Refactor test by @mjwen in #125
- Update the ptemcee dependency by @yonatank93 in #137
- Update GH actions to use latest conda-forge kim-api and test on macOS by @mjwen in #143
- Recreate docs building codes by @mjwen in #129
- Fix neighbor list bug by @mjwen in #90
- Fix _WrapperCalculator by @mjwen in #95
- Remove requirements.txt, add info in setup.py by @mjwen in #108
- Add multiple species support of LJ by @mjwen in #112
- Update CI to fix cmake version by @mjwen in #117
- WIP: Implement bootstrap by @yonatank93 in #107
- Uncertainty quantification via MCMC (@yonatank93). New tutorial and explanation of the functionality provided in the doc.
- Issue and PR template
- Linear regression model parameter shape
- NN multispecies calculator to use parameters of all models
- Documentation on installing KLIFF and dependencies
- Add ParameterTransform class to transform parameters into a different space (e.g. log space) @yonatank93
- Add Weight class to set weight for energy/forces/stress. This is not backward
compatible, which changes the signature of the residual function. Previously, in a
residual function, the weights are passed in via the
data
argument, but now, its passed in via an instance of the Weight class. @yonatank93
- Fix checking cutoff entry @adityakavalur
- Fix energy_residual_fn and forces_residual_fn to weigh different component
- Change to use precommit GH action to check code format
- Fix neighlist (even after v0.3.2, the problem can still happen). Now neighlist is the same as kimpy
- Enable params_relation_callback() for KIM model
- Fix neighbor list segfault due to numerical error for 1D and 2D cases
- add gpu training for NN model; set the
gpu
parameter of a calculator (e.g.CalculatorTorch(model, gpu=True)
) to use it - add pyproject.toml, requirements.txt, dependabot.yml to config repo
- switch to
furo
doc theme - changed: compute grad of energy wrt desc in batch mode (NN calculator)
- fix: set
fingerprints_filename
and load descriptor state dict when reuse fingerprints (NN calculator)
- change license to LGPL
- set default optimizer
- put
kimpy
code intry except
block - add
state_dict
for descriptors and save it together with model - change to use
loguru
for logging and allows user to set log level
- update to be compatible with
kimpy v2.0.0
- update to be compatible with
kimpy v2.0.0
- use entry
entry_points
to handle command line tool - rename
utils
todevtool
- add type hint for all codes
- reorganize model and parameters to make it more robust
- add more docstring for many undocumented class and functions
- add GitHub actions to automatically deploy to PyPI
- add a simple example to README
- add neighborlist utility, making NN model independent on kimpy
- add calculator to deal with multiple species for NN model
- update dropout layer to be compatible with the pytorch 1.3
- add support for the geodesic Levenberg-Marquardt minimization algorithm
- add command line tool
model
to inquire available parameters of KIM model
- add RMSE and Fisher information analyzers
- allow configuration weight for ML models
- add write optimizer state dictionary for ML models
- combine functions
generate_training_fingerprints()
andgenerate_test_fingerprints()
of descriptor togenerate_fingerprints()
(supporting passing mean and stdev file) - rewrite symmetry descriptors to share with KIM driver
- MPI parallelization for physics-based models
- reorganize machine learning related files
- various bug fixes
- API changes * class
DataSet
renamed toDataset
* classCalculator
moved to modulecalculators
from modulecalculator
- KLIFF available from PyPI now. Using
$pip install kliff
to install. - Use SW model from the KIM website in tutorial.
- Format code with
black
.
First official release, but API is not guaranteed to be stable.
- Add more docs to {ref}
reference
.
Pre-release.
- Uncertainty quantification via MCMC (@yonatank93). New tutorial and explanation of the functionality provided in the doc.
- Issue and PR template
- Linear regression model parameter shape
- NN multispecies calculator to use parameters of all models
- Documentation on installing KLIFF and dependencies
- Add ParameterTransform class to transform parameters into a different space (e.g. log space) @yonatank93
- Add Weight class to set weight for energy/forces/stress. This is not backward
compatible, which changes the signature of the residual function. Previously, in a
residual function, the weights are passed in via the
data
argument, but now, its passed in via an instance of the Weight class. @yonatank93
- Fix checking cutoff entry @adityakavalur
- Fix energy_residual_fn and forces_residual_fn to weigh different component
- Change to use precommit GH action to check code format
- Fix neighlist (even after v0.3.2, the problem can still happen). Now neighlist is the same as kimpy
- Enable params_relation_callback() for KIM model
- Fix neighbor list segfault due to numerical error for 1D and 2D cases
- add gpu training for NN model; set the
gpu
parameter of a calculator (e.g.CalculatorTorch(model, gpu=True)
) to use it - add pyproject.toml, requirements.txt, dependabot.yml to config repo
- switch to
furo
doc theme - changed: compute grad of energy wrt desc in batch mode (NN calculator)
- fix: set
fingerprints_filename
and load descriptor state dict when reuse fingerprints (NN calculator)
- change license to LGPL
- set default optimizer
- put
kimpy
code intry except
block - add
state_dict
for descriptors and save it together with model - change to use
loguru
for logging and allows user to set log level
- update to be compatible with
kimpy v2.0.0
- update to be compatible with
kimpy v2.0.0
- use entry
entry_points
to handle command line tool - rename
utils
todevtool
- add type hint for all codes
- reorganize model and parameters to make it more robust
- add more docstring for many undocumented class and functions
- add GitHub actions to automatically deploy to PyPI
- add a simple example to README
- add neighborlist utility, making NN model independent on kimpy
- add calculator to deal with multiple species for NN model
- update dropout layer to be compatible with the pytorch 1.3
- add support for the geodesic Levenberg-Marquardt minimization algorithm
- add command line tool
model
to inquire available parameters of KIM model
- add RMSE and Fisher information analyzers
- allow configuration weight for ML models
- add write optimizer state dictionary for ML models
- combine functions
generate_training_fingerprints()
andgenerate_test_fingerprints()
of descriptor togenerate_fingerprints()
(supporting passing mean and stdev file) - rewrite symmetry descriptors to share with KIM driver
- MPI parallelization for physics-based models
- reorganize machine learning related files
- various bug fixes
- API changes * class
DataSet
renamed toDataset
* classCalculator
moved to modulecalculators
from modulecalculator
- KLIFF available from PyPI now. Using
$pip install kliff
to install. - Use SW model from the KIM website in tutorial.
- Format code with
black
.
First official release, but API is not guaranteed to be stable.
- Add more docs to {ref}
reference
.
Pre-release.
- Refactor test by @mjwen in #125
- Update the ptemcee dependency by @yonatank93 in #137
- Update GH actions to use latest conda-forge kim-api and test on macOS by @mjwen in #143
- Recreate docs building codes by @mjwen in #129
- Fix neighbor list bug by @mjwen in #90
- Fix _WrapperCalculator by @mjwen in #95
- Remove requirements.txt, add info in setup.py by @mjwen in #108
- Add multiple species support of LJ by @mjwen in #112
- Update CI to fix cmake version by @mjwen in #117
- WIP: Implement bootstrap by @yonatank93 in #107
- Uncertainty quantification via MCMC (@yonatank93). New tutorial and explanation of the functionality provided in the doc.
- Issue and PR template
- Linear regression model parameter shape
- NN multispecies calculator to use parameters of all models
- Documentation on installing KLIFF and dependencies
- Add ParameterTransform class to transform parameters into a different space (e.g. log space) @yonatank93
- Add Weight class to set weight for energy/forces/stress. This is not backward
compatible, which changes the signature of the residual function. Previously, in a
residual function, the weights are passed in via the
data
argument, but now, its passed in via an instance of the Weight class. @yonatank93
- Fix checking cutoff entry @adityakavalur
- Fix energy_residual_fn and forces_residual_fn to weigh different component
- Change to use precommit GH action to check code format
- Fix neighlist (even after v0.3.2, the problem can still happen). Now neighlist is the same as kimpy
- Enable params_relation_callback() for KIM model
- Fix neighbor list segfault due to numerical error for 1D and 2D cases
- add gpu training for NN model; set the
gpu
parameter of a calculator (e.g.CalculatorTorch(model, gpu=True)
) to use it - add pyproject.toml, requirements.txt, dependabot.yml to config repo
- switch to
furo
doc theme - changed: compute grad of energy wrt desc in batch mode (NN calculator)
- fix: set
fingerprints_filename
and load descriptor state dict when reuse fingerprints (NN calculator)
- change license to LGPL
- set default optimizer
- put
kimpy
code intry except
block - add
state_dict
for descriptors and save it together with model - change to use
loguru
for logging and allows user to set log level
- update to be compatible with
kimpy v2.0.0
- update to be compatible with
kimpy v2.0.0
- use entry
entry_points
to handle command line tool - rename
utils
todevtool
- add type hint for all codes
- reorganize model and parameters to make it more robust
- add more docstring for many undocumented class and functions
- add GitHub actions to automatically deploy to PyPI
- add a simple example to README
- add neighborlist utility, making NN model independent on kimpy
- add calculator to deal with multiple species for NN model
- update dropout layer to be compatible with the pytorch 1.3
- add support for the geodesic Levenberg-Marquardt minimization algorithm
- add command line tool
model
to inquire available parameters of KIM model
- add RMSE and Fisher information analyzers
- allow configuration weight for ML models
- add write optimizer state dictionary for ML models
- combine functions
generate_training_fingerprints()
andgenerate_test_fingerprints()
of descriptor togenerate_fingerprints()
(supporting passing mean and stdev file) - rewrite symmetry descriptors to share with KIM driver
- MPI parallelization for physics-based models
- reorganize machine learning related files
- various bug fixes
- API changes * class
DataSet
renamed toDataset
* classCalculator
moved to modulecalculators
from modulecalculator
- KLIFF available from PyPI now. Using
$pip install kliff
to install. - Use SW model from the KIM website in tutorial.
- Format code with
black
.
First official release, but API is not guaranteed to be stable.
- Add more docs to {ref}
reference
.
Pre-release.
- Uncertainty quantification via MCMC (@yonatank93). New tutorial and explanation of the functionality provided in the doc.
- Issue and PR template
- Linear regression model parameter shape
- NN multispecies calculator to use parameters of all models
- Documentation on installing KLIFF and dependencies
- Add ParameterTransform class to transform parameters into a different space (e.g. log space) @yonatank93
- Add Weight class to set weight for energy/forces/stress. This is not backward
compatible, which changes the signature of the residual function. Previously, in a
residual function, the weights are passed in via the
data
argument, but now, its passed in via an instance of the Weight class. @yonatank93
- Fix checking cutoff entry @adityakavalur
- Fix energy_residual_fn and forces_residual_fn to weigh different component
- Change to use precommit GH action to check code format
- Fix neighlist (even after v0.3.2, the problem can still happen). Now neighlist is the same as kimpy
- Enable params_relation_callback() for KIM model
- Fix neighbor list segfault due to numerical error for 1D and 2D cases
- add gpu training for NN model; set the
gpu
parameter of a calculator (e.g.CalculatorTorch(model, gpu=True)
) to use it - add pyproject.toml, requirements.txt, dependabot.yml to config repo
- switch to
furo
doc theme - changed: compute grad of energy wrt desc in batch mode (NN calculator)
- fix: set
fingerprints_filename
and load descriptor state dict when reuse fingerprints (NN calculator)
- change license to LGPL
- set default optimizer
- put
kimpy
code intry except
block - add
state_dict
for descriptors and save it together with model - change to use
loguru
for logging and allows user to set log level
- update to be compatible with
kimpy v2.0.0
- update to be compatible with
kimpy v2.0.0
- use entry
entry_points
to handle command line tool - rename
utils
todevtool
- add type hint for all codes
- reorganize model and parameters to make it more robust
- add more docstring for many undocumented class and functions
- add GitHub actions to automatically deploy to PyPI
- add a simple example to README
- add neighborlist utility, making NN model independent on kimpy
- add calculator to deal with multiple species for NN model
- update dropout layer to be compatible with the pytorch 1.3
- add support for the geodesic Levenberg-Marquardt minimization algorithm
- add command line tool
model
to inquire available parameters of KIM model
- add RMSE and Fisher information analyzers
- allow configuration weight for ML models
- add write optimizer state dictionary for ML models
- combine functions
generate_training_fingerprints()
andgenerate_test_fingerprints()
of descriptor togenerate_fingerprints()
(supporting passing mean and stdev file) - rewrite symmetry descriptors to share with KIM driver
- MPI parallelization for physics-based models
- reorganize machine learning related files
- various bug fixes
- API changes * class
DataSet
renamed toDataset
* classCalculator
moved to modulecalculators
from modulecalculator
- KLIFF available from PyPI now. Using
$pip install kliff
to install. - Use SW model from the KIM website in tutorial.
- Format code with
black
.
First official release, but API is not guaranteed to be stable.
- Add more docs to {ref}
reference
.
Pre-release.