Update README.md

README.md

@@ -1,67 +1,6 @@
----
-extra_gated_prompt: "You agree to not use the dataset to conduct experiments that cause harm to human subjects."
-extra_gated_fields:
-  Company: text
-  Name: text
-  Position: text
-  Purpose_of_data: text
-  Contact_Email: text
-  Country: country
-  Specific_date: date_picker
-
-  Supervisor_Name (If not applicable, please enter 'None'):
-    type: text
-
-  Supervisor_Email (If not applicable, please enter 'None'):
-    type: text
-
-  I want to use this dataset for:
-    type: select
-    options:
-      - Research
-      - Education
-      - label: Other
-        value: other
-
-  I agree to use this dataset for non-commercial use ONLY: checkbox
-
-
-extra_gated_heading: "Acknowledge license to accept the repository"
-extra_gated_description: "Our team may take 2–3 days to process your request"
-extra_gated_button_content: "Acknowledge license"
----
-
-**This dataset is part of ongoing work and is not shared at the current stage.**
-
-
-**Please provide your real name and institutional email for verification.**  
-If you experience any issues, contact Mr. Cao at: 📧 **bcao686@connect.hkust-gz.edu.cn**  
-Fake names or unverifiable information will result in your request being denied.
-
-# CPPbenchmark
-
-
-**CPPbenchmark** is a curated benchmark suite for evaluating machine learning models on crystal property prediction (CPP) tasks. It includes eight tasks—seven regression (e.g., formation energy, band gap, elastic moduli) and one classification (metal/non-metal)—using high-quality datasets derived from the Materials Project. All data is provided in ASE `.db` format, enabling easy integration with atomistic ML workflows.
-
-
-
-
-## 📝 Access Request Form
-
-> Please fill in the form truthfully. We will review your request within **2–3 business days**.
-
 
 
-
-## 📥 Access & Usage
-
-- All required information must be filled out truthfully.
-- Personal verification typically takes **2 working days**.
-- Once approved, you'll be granted download access to the full database.
-
----
-
-## 📚 Citation & License
+## Citation & License
 
 **Commercial use is strictly prohibited.**  
 All access will be logged.  
@@ -77,21 +16,27 @@ If you use this dataset in your research, please cite **all** the following work
   publisher={Springer}
 }
 
-@inproceedings{binsimxrd,
+@article{binsimxrd,
   title={SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal Symmetry Classification Benchmark},
-  author={Bin, CAO and Liu, Yang and Zheng, Zinan and Tan, Ruifeng and Li, Jia and Zhang, Tong-yi},
+  author={Cao, Bin and Liu, Yang and Zheng, Zinan and Tan, Ruifeng and Li, Jia and Zhang, Tong-yi},
   booktitle={The Thirteenth International Conference on Learning Representations}
 }
 
 
-@misc{caobin_2025cpp,
-	author       = {Bin Cao and Tong-Yi Zhang },
-	title        = { CPPbenchmark (Revision 261622f) },
-	year         = 2025,
-	url          = { https://huggingface.co/datasets/caobin/CPPbenchmark },
-	doi          = { 10.57967/hf/5378 },
-	publisher    = { Hugging Face }
+
+@article{cao2025beyond,
+  title={Beyond Structure: Invariant Crystal Property Prediction with Pseudo-Particle Ray Diffraction},
+  author={Cao, Bin and Liu, Yang and Zhang, Longhan and Wu, Yifan and Li, Zhixun and Luo, Yuyu and Cheng, Hong and Ren, Yang and Zhang, Tong-Yi},
+  booktitle={The Thirteenth International Conference on Learning Representations},
+  year={2026}
 }
+
+
+
+
+
+
+
 ```
 
 
@@ -101,7 +46,7 @@ This benchmark includes **6 primary tasks** for evaluating CPP models in crystal
 
 ### **Mission Settings**
 
-#### 🔢 Regression Tasks
+#### Regression Tasks
 
 1. **T1**: Formation Energy Prediction (`float`) | `eV/atom`
 2. **T2**: Band Gap Prediction (`float`) | `eV`
@@ -111,7 +56,7 @@ This benchmark includes **6 primary tasks** for evaluating CPP models in crystal
 
 
 
-#### 🧮 Classification Task
+#### Classification Task
 
 7. **T6**: Metal/Non-metal Classification (`int`, binary classification)
 
@@ -121,7 +66,7 @@ This benchmark includes **6 primary tasks** for evaluating CPP models in crystal
 
 The datasets are hosted on Hugging Face and stored in ASE database (`.db`) format. Each database includes crystal structures and corresponding property labels.
 
-### 📁 `fe_bandg/`
+### `fe_bandg/`
 
 * **Train:** `MP_100_bgfe_train.db` (86,071 crystals)
 * **Val:** `MP_100_bgfe_val.db` (12,295 crystals)
@@ -130,7 +75,7 @@ The datasets are hosted on Hugging Face and stored in ASE database (`.db`) forma
 
   * Keys: `formation_energy`, `band_gap`
 
-### 📁 `modulus/`
+### `modulus/`
 
 * **Train:** `MP_modulus_train.db` (6,631 crystals)
 * **Val:** `MP_modulus_val.db` (947 crystals)
@@ -139,7 +84,7 @@ The datasets are hosted on Hugging Face and stored in ASE database (`.db`) forma
 
   * Keys: `bulk_modulus`, `shear_modulus`, `youngs_modulus`, `poissons_ratio`, 
 
-### 📁 `metal_nometal/`
+### `metal_nometal/`
 
 * **Train:** `MP_100_metal_train.db` (86,071 crystals)
 * **Val:** `MP_100_metal_val.db` (12,295 crystals)
@@ -158,7 +103,7 @@ In preparing the dataset for model training, we applied a log10 transformation t
 
 ---
 
-## 🧪 Example: Reading Data from an ASE Database
+## Example: Reading Data from an ASE Database
 
 You can read data using the `ase.db` module as follows: