fix minor issues in README and evaluation script

README.md

@@ -70,7 +70,7 @@ The repo currently contains the following contents:
 
 # Dataset Usage
 
-The sample dataset contains the `main` dataset (the dataset for training, validation and in-distribution testing) and the `ood-test` dataset.
+The `dataset` folder of the repo contains the `main` dataset (the dataset for training, validation and in-distribution testing) and the `ood-test` dataset.
 
 Each dataset contains several `parts`, each of which corresponds to a specific piece of information. The parts are:
 
@@ -81,7 +81,7 @@ Each dataset contains several `parts`, each of which corresponds to a specific p
 * `auxdensity.denfit.etb2.0`: the density coefficients on the ETB basis of def2-svp with $\beta=2.0$.
 * `auxdensity.denfit.etb1.5`: the density coefficients on the ETB basis of def2-svp with $\beta=1.5$.
 
-Example:
+Example usage:
 
 ```python
 from dataset import SCFBenchDataset
@@ -102,7 +102,22 @@ dataset.dataset[0].keys()
 
 # Evaluation
 
-To evaluate 
+The full evaluation code is provided in `evaluate_scf_gpu.py`. To evaluate the provided NequIP model checkpoint `nequip_L_jfit.ckpt`, run:
+
+```bash
+# evaluate on the test split of the main dataset (ID setting); note that you can also specify --num-shards and --shard-index to only run the evaluation on a subset
+python evaluate_scf_gpu.py --ckpt nequip_L_jfit.ckpt --data-root dataset/main --split test --output id_test.csv
+
+# evaluate on the ood-test dataset
+python evaluate_scf_gpu.py --ckpt nequip_L_jfit.ckpt --data-root dataset/ood-test --split no --output ood_test.csv
+
+# evaluate on the ood-test dataset, with a XC/basis transfer setting
+python evaluate_scf_gpu.py --ckpt nequip_L_jfit.ckpt --data-root dataset/ood-test --split no --xc blyp --transfer-basis def2-tzvp --output ood_test_transfer.csv
+```
+
+# Known Issues
+
+* The species-wise linear readout layer may cause unstable model forward time on GPU in some software environments. We recommend implementing the prediction using a uniform padded basis for all species, just like how Hamiltonian prediction models (e.g., QHNet) predict the Hamiltonian matrix. We have tested this method internally but would like to keep the code in this repo consistent with our paper for reproducibility.
 
 
 # Citing SCFbench
@@ -130,4 +145,4 @@ Our modified version, the SCFbench dataset, is also licensed under [CC BY-SA 3.0
 
 ## About [ByteDance Seed Team](https://seed.bytedance.com/)
 
-Founded in 2023, ByteDance Seed Team is dedicated to crafting the industry's most advanced AI foundation models. The team aspires to become a world-class research team and make significant contributions to the advancement of science and society.
+Founded in 2023, ByteDance Seed Team is dedicated to crafting the industry's most advanced AI foundation models. The team aspires to become a world-class research team and make significant contributions to the advancement of science and society.

evaluate_scf_gpu.py

@@ -341,17 +341,17 @@ def main():
     parser.add_argument('--normalize-nelec', action='store_true', help='Normalize the model prediction by number of electrons.')
     args = parser.parse_args()
 
-    if not args.gt_mode:
-        assert args.ckpt is not None, 'Checkpoint path is required in non-gt mode.'
-
+    if args.ckpt is not None:
+        # if a ckpt is provided, use the data config in the ckpt
         ckpt = torch.load(args.ckpt, map_location='cpu', weights_only=True)
-
         config = ckpt['config']
-        model = nequip_simple_builder(**config['model'])
-        model.load_state_dict(ckpt['state_dict'])
-        model = model.eval().cuda()
         data_config = config['data']
+        if not args.gt_mode:
+            model = nequip_simple_builder(**config['model'])
+            model.load_state_dict(ckpt['state_dict'])
+            model = model.eval().cuda()
     else:
+        assert args.gt_mode, 'Checkpoint path is required in non-gt mode.'
         gt_mode_part = 'auxdensity.denfit' if args.model_type == 'auxdensity' else args.model_type
         data_config = {
             'r_max': 5.0,
@@ -371,7 +371,7 @@ def main():
     else:
         dataset_indices = list(range(len(dataset)))
 
-    dataset_subset_indices = [i for i in dataset_indices if i % args.num_shards == args.shard_index]
+    dataset_subset_indices = [j for i, j in enumerate(dataset_indices) if i % args.num_shards == args.shard_index]
     dataloader = DataLoader(
         Subset(dataset, dataset_subset_indices),
         batch_size=1,