---
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.