Tweaked README.md and changed permissions on source

src/MLIAP/README

@@ -1,57 +0,0 @@
-This package provides a general interface to families of
-machine-learning interatomic potentials (MLIAPs). This interface consists
-of a mliap pair style and a mliap compute.
-
-The mliap pair style is used when running simulations with energies and
-forces calculated by an MLIAP. The interface allows separate
-definitions of the interatomic potential functional form (*model*)
-and the geometric quantities that characterize the atomic positions
-(*descriptor*). By defining *model* and *descriptor* separately,
-it is possible to use many different models with a given descriptor,
-or many different descriptors with a given model. The pair_style
-supports the following models: *linear*, *quadratic*, and
-*mliappy* (general Python interface to things like PyTorch, see below
-for build instructions).
-It currently supports only one class of descriptors,
-*sna*, the SNAP descriptors, including the
-linear, quadratic, and chem variants.
-It is straightforward to add new descriptor and model
-styles.
-
-The mliap compute style provides gradients of the energy, force,
-and stress tensor w.r.t. model parameters.
-These are useful when training MLIAPs to match target data.
-Any *model or *descriptor* that has been implemented for the
-*mliap* pair style can also be accessed by the *mliap* compute.
-In addition to the energy, force, and stress gradients, w.r.t.
-each *model* parameter, the compute also calculates the energy,
-force, and stress contributions from a user-specified
-reference potential.
-
-To see how this command
-can be used within a Python workflow to train machine-learning interatomic
-potentials, see the examples in FitSNAP https://github.com/FitSNAP/FitSNAP.
-
-*Additional instructions for building MLIAP with Python support enabled*
-
-The *mliappy* energy model requires that the MLIAP package
-be compiled with Python support enabled.
-This extension, written by Nick Lubbers (LANL),
-provides a coupling to PyTorch and other Python modules.
-This should be automatically
-enabled by default if the prerequisite software is installed.  It can be
-enforced during CMake configuration by setting the variable
-MLIAP_ENABLE_PYTHON=yes or for conventional build by adding -DMLIAP_PYTHON
-to the LMP_INC variable in your makefile and running the cythonize script
-on the .pyx file(s) copied to the src folder.
-
-This requires to also install the PYTHON package and have the cython
-(https://cython.org) software installed.  During configuration/compilation
-the cythonize script will be used to convert the provided .pyx file(s)
-to C++ code.  Please do not run the cythonize script in the src/MLIAP folder.
-If you have done so by accident, you need to delete the generated .cpp and .h
-file(s) in the src/MLIAP folder or there may be problems during compilation.
-
-More information on building LAMMPS with this package is here:
-
-https://lammps.sandia.gov/doc/Build_extras.html#mliap

src/MLIAP/README.md

@@ -28,7 +28,7 @@ force, and stress contributions from a user-specified
 reference potential.
 
 ## Generating the model files from the third-party packages
-- To train the `linear` and `quardratic` model with the SNAP descritptors, see the examples in [FitSNAP](https://github.com/FitSNAP/FitSNAP).
+- To train the `linear` and `quadratic` models with the SNAP descriptors, see the examples in [FitSNAP](https://github.com/FitSNAP/FitSNAP).
 - To train the `nn` model with the SNAP descriptors, check the examples in [PyXtal\_FF](https://github.com/qzhu2017/PyXtal_FF).
 
 ## Building MLIAP with Python support