release 2020.08

Author: amirhajibabaei

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2019 amir
+Copyright (c) 2019 Amir Hajibabaei T.
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -1 +1,73 @@
-To be completed ...
+<!-- #region -->
+### Introduction
+This is a package for machine learning (ML) of the potential energy surface (PES)
+from costly density functional theory (DFT) calculations using the Gaussian process 
+regression method with emphasis on the sparse approximations.
+Ab-initio calculations such as AIMD, NEB, etc can be substantially accelerated by 
+fast ML models built on-the-fly.
+Moreover, the ML models built with smaller size of physical systems can be applied
+for simulations of larger systems which are impossible with DFT.
+In principle, all the calculators supported by the atomic simulation environment 
+([ASE](https://wiki.fysik.dtu.dk/ase/)) can be modeled.
+
+
+### Dependencies
+Main:
+* numpy 
+* scipy
+* pytorch
+* atomic simulation environment (ASE)
+
+Recommended: 
+* message passing interface (MPI enabled pytorch) for distributed computation.
+
+Optional:
+* pymatgen 
+* spglib 
+* mendeleev 
+* matplotlib
+* nglview
+
+
+### Usage
+It wraps ASE calculators:
+
+```python
+from theforce.calculator.active import ActiveCalculator
+
+# atoms = see ASE docs
+# main_calc = see ASE calculators
+# kernel = see the proceeding
+
+calc = ActiveCalculator(main_calc, kernel)
+atoms.set_calculator(calc)
+
+# proceed with desired calculations
+```
+
+---
+
+Kernels can be imported from `theforce.similarity`.
+`UniversalSoapKernel` is the easiest to implement:
+```python
+from theforce.similarity.universal import UniversalSoapKernel
+
+lmax, nmax, exponent, cutoff = 3, 3, 4, 6.
+kernel = UniversalSoapKernel(lmax, nmax, exponent, cutoff)
+```
+but it maybe slow in some cases. 
+
+---
+
+Another option is `HeterogeneousSoapKernel` which segregates atomic species, 
+is faster (~10 times), but it uses more memory.
+A utility function is present for creating the relevent list of kernels given 
+the atomic species present in the system:
+```python
+from theforce.run.fly import default_kernel
+
+# numbers = [1, 8] for water
+# cutoff, etc can be given as kwargs
+kernel = default_kernel(numbers) 
+```
+<!-- #endregion -->

setup.py

@@ -1,13 +1,15 @@
+# +
 from distutils.core import setup
 
+
 setup(
     name="theforce",
-    version="0.0.1",
+    version="2020.08",
     author="Amir HajiBabaei T.",
     author_email="a.hajibabaei.86@gmail.com",
-    description="Machine Learning of the forces on atoms",
+    description="machine learning of the potential energy surface from DFT",
     url="https://github.com",
-    package_dir={'theforce':'theforce'},
+    package_dir={'theforce': 'theforce'},
     packages=['theforce'],
     classifiers=[
         "Programming Language :: Python :: 3",