Merge pull request #20 from CharlesCNorton/patch-1

Fix typos in README

Author: danielzuegner

README.md

@@ -96,7 +96,7 @@ export RESULTS_PATH="results/$MODEL_NAME/"  # Samples will be written to this di
 python scripts/generate.py $RESULTS_PATH $MODEL_PATH --batch_size=16 --checkpoint_epoch=last --properties_to_condition_on="{'dft_mag_density': 0.15}" --diffusion_guidance_factor=2.0
 ```
 > [!TIP]
-> The argument `--diffusion-guidance-factor` corresponds to the $\gamma$ parameter in [classifier-free diffusion guidance](https://sander.ai/2022/05/26/guidance.html). Setting it to zero corresponds to unconditional generation, and increasing it further tends to produce samples which adhere more to the input property values, though at the expense of diversity and realisticness of samples.
+> The argument `--diffusion-guidance-factor` corresponds to the $\gamma$ parameter in [classifier-free diffusion guidance](https://sander.ai/2022/05/26/guidance.html). Setting it to zero corresponds to unconditional generation, and increasing it further tends to produce samples which adhere more to the input property values, though at the expense of diversity and realism of samples.
 
 ### Multiple property-conditioned generation
 You can also generate materials conditioned on more than one property. For instance, you can use the pre-trained model located at `checkpoints/chemical_system_energy_above_hull` to generate conditioned on chemical system and energy above the hull, or the model at `checkpoints/dft_mag_density_hhi_score` for joint conditioning on [HHI score](https://en.wikipedia.org/wiki/Herfindahl%E2%80%93Hirschman_index) and magnetic density.
@@ -109,13 +109,13 @@ python scripts/generate.py $RESULTS_PATH $MODEL_PATH --batch_size=16 --checkpoin
 ```
 ## Evaluation
 
-Once you have generated a list of structures contained in `$RESULTS_PATH` (either using MatterGen or another method), you can  relax the structures using the default MatterSim machine learning force field (see [repository](https://github.com/microsoft/mattersim)) and compute novelty, uniqueness, stability (using energy estimated by MatterSim), and other metrics via the following command:
+Once you have generated a list of structures contained in `$RESULTS_PATH` (either using MatterGen or another method), you can relax the structures using the default MatterSim machine learning force field (see [repository](https://github.com/microsoft/mattersim)) and compute novelty, uniqueness, stability (using energy estimated by MatterSim), and other metrics via the following command:
 ```bash
 python scripts/evaluate.py --structures_path=$RESULTS_PATH --relax=True --structure_matcher='disordered' --save_as="$RESULTS_PATH/metrics.json"
 ```
 This script will write `metrics.json` containing the metric results to `$RESULTS_PATH` and will print it to your console.
 > [!IMPORTANT]
-> The evaluation script in this repository uses [MatterSim](https://github.com/microsoft/mattersim), a machine-learning force field (MLFF) to relax structures and assess their stability via MatterSim's predicted energies. While this is orders of magnitudes faster than evaluation via density functional theory (DFT), doesn't require a license to run the evaluation, and typically has a high accuracy, there are important caveats. (1) In the MatterGen publication we use DFT to evaluate structures generated by all models and baselines; (2) DFT is more accurate and reliable, particularly in less common chemical systems. Thus, evaluation results obtained with this evaluation code may give different results than DFT evaluation; and we recommend to confirm results obtained with MLFFs with DFT before drawing conclusions. 
+> The evaluation script in this repository uses [MatterSim](https://github.com/microsoft/mattersim), a machine-learning force field (MLFF) to relax structures and assess their stability via MatterSim's predicted energies. While this is orders of magnitude faster than evaluation via density functional theory (DFT), it doesn't require a license to run the evaluation, and typically has a high accuracy, there are important caveats. (1) In the MatterGen publication we use DFT to evaluate structures generated by all models and baselines; (2) DFT is more accurate and reliable, particularly in less common chemical systems. Thus, evaluation results obtained with this evaluation code may give different results than DFT evaluation; and we recommend to confirm results obtained with MLFFs with DFT before drawing conclusions. 
 
 > [!TIP]
 > By default, this uses `MatterSim-v1-1M`. If you would like to use the larger `MatterSim-v1-5M` model, you can add the `--potential_load_path="MatterSim-v1.0.0-5M.pth"` argument. You may also check the [MatterSim repository](https://github.com/microsoft/mattersim) for the latest version of the model.