Update README.md

README.md

@@ -11,7 +11,7 @@ conda env create -f env.yml
 ```
 however if you intend to use inference models from the Open Catalyst Project follow the install guide [here](https://fair-chem.github.io/core/install.html).
 
-After environment creatio navigate to this folder and install *cheatools*:
+After environment creation navigate to this folder and install *cheatools*:
 ```terminal
 pip install -e .
 ```
@@ -20,10 +20,10 @@ pip install -e .
 The *examples* folder contains working examples of 
 All modules contain further explanation and instructions within each subdirectory. Data have been provided so that each module contains a working example.
 
-The [run_dft](examples/run_dft) demonstrates querying DFT calculations used to train the inference algorithms. This aids in sampling multiple binding sites on the same slab to minimize compute per adsorption energy optained.
+[run_dft](examples/run_dft) demonstrates querying DFT calculations used to train the inference algorithms. This aids in sampling multiple binding sites on the same slab to minimize compute per adsorption energy optained.
 
-The [train_lgnn](examples/train_lgnn) reduces the optimized geometries from the DFT calculations to graph features and subsequently trains a lean graph neural network (lGNN) to perform adsorption energy inference.
+[train_lgnn](examples/train_lgnn) reduces the optimized geometries from the DFT calculations to graph features and subsequently trains a lean graph neural network (lGNN) to perform adsorption energy inference.
 
-The [surface_simulation](examples/surface_simulation) emulates a solid-solution alloy surface via a grid-based approach. This surrogate surface is used in conjunction with the lGNN to infer the distribution of adsorption energies on the surface. Additionally, competitive co-adsorption of different species can be included for certain sites.
+[surface_simulation](examples/surface_simulation) emulates a solid-solution alloy surface via a grid-based approach. This surrogate surface is used in conjunction with the lGNN to infer the distribution of adsorption energies on the surface. Additionally, competitive co-adsorption of different species can be included for certain sites.
 
-The bayesian_optimization(Coming Soon!) applies the above step in a Bayesian optimization procedure to maximize a catalytic activity by sampling surfaces within a specified composition space.
+bayesian_optimization (Coming Soon!) applies the above step in a Bayesian optimization procedure to maximize a catalytic activity by sampling surfaces within a specified composition space.