Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- bigai/TongSIM-Asset
+language:
+- en
+metrics:
+- exact_match
+new_version: zai-org/GLM-4.7
+pipeline_tag: reinforcement-learning
+library_name: transformers
+tags:
+- physics
+- chemistry
+- deepmind
+---
+
+
+# PsiFormer Checkpoint: Hydrogen → Oxygen
+
+This repository contains pretrained **PsiFormer** checkpoints for electronic-structure modeling across atomic systems ranging from **Hydrogen (Z=1)** to **Oxygen (Z=8)**.
+
+The model is designed for **variational quantum Monte Carlo (VMC)**–style wavefunction modeling, with a Transformer-based architecture that captures electron–electron correlations efficiently and scalably.
+
+---
+
+## Model Overview
+
+- **Architecture**: PsiFormer (Transformer-based wavefunction ansatz)
+- **Task**: Electronic wavefunction approximation
+- **Method**: Variational Monte Carlo (VMC)
+- **Atomic range**: Hydrogen → Oxygen
+- **Framework**: PyTorch
+- **Precision**: FP32 (unless otherwise specified)
+
+The model outputs parameters of a many-body wavefunction that can be used to estimate ground-state energies and other observables via Monte Carlo sampling.
+
+---
+
+## Training Details
+
+- **Systems**: Isolated atoms with atomic numbers Z = 1–8
+- **Electrons**: Corresponding neutral configurations
+- **Optimization**: Stochastic gradient–based optimization of variational energy
+- **Sampling**: Metropolis–Hastings MCMC
+- **Objective**: Minimize the expectation value of the Hamiltonian
+
+Exact hyperparameters (learning rate, batch size, number of walkers, etc.) should be considered checkpoint-specific and are documented in the accompanying configuration files when available.
+
+---
+
+## Intended Use
+
+This checkpoint is intended for:
+
+- Initializing PsiFormer models for light atoms
+- Transfer learning to larger atoms or small molecules
+- Benchmarking neural quantum states
+- Research and educational purposes in computational quantum physics
+
+It is **not** intended for production chemistry workflows without further validation.
+
+---
+
+## Example Usage
+
+```python
+import torch
+from psiformer import PsiFormer
+
+model = PsiFormer(...)
+state_dict = torch.load("psiformer_h_to_o.pt", map_location="cpu")
+model.load_state_dict(state_dict)
+model.eval()
+````
+
+Refer to the PsiFormer repository for full examples including sampling and energy evaluation.
+
+---
+
+## Limitations
+
+* Trained only on **isolated atoms**, not molecules
+* Accuracy degrades outside the Z = 1–8 range
+* Performance depends strongly on sampling quality and optimization setup
+* No relativistic or spin–orbit effects included
+
+---
+
+## Citation
+
+If you use this checkpoint in academic work, please cite the corresponding PsiFormer paper or repository.
+
+```bibtex
+@misc{psiformer,
+  title={PsiFormer: Transformer-based Neural Quantum States},
+  author={...},
+  year={202X}
+}
+```
+
+---
+
+## License
+
+Specify the license here (e.g. MIT, Apache 2.0, custom research license).
+
+---
+
+## Contact
+
+For questions, issues, or collaborations, please open an issue in the main PsiFormer repository.
+
+
+