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
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.
@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.