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DeepRSH: Deep Learning Range-Separated Hybrid Functionals

A deep learning framework for training range-separated hybrid (RSH) density functionals using JAX and grad_dft.

Overview

DeepRSH implements neural network-based exchange-correlation functionals that combine:

  • Traditional XC energy densities (LDA, B88, VWN, LYP)
  • Hartree-Fock exact exchange with range separation
  • Learned mixing coefficients via neural networks

Features

  • Range-Separated Hybrids: Supports multiple omega parameters for long-range exact exchange
  • Trainable Functionals: End-to-end differentiable implementation using JAX
  • GAT Architecture: Graph Attention Networks for molecular representation
  • H₂ Dissociation: Specialized datasets for diatomic molecules

Installation

# Install dependencies
pip install jax jaxlib flax optax grad_dft pyscf

# Or use conda
conda create -n deeprsh python=3.10
conda activate deeprsh
pip install -r requirements.txt

Usage

Creating a Functional

from xc_functional import rsh_b3lyp_nn
from grad_dft import energy_predictor

# Create the functional
functional = rsh_b3lyp_nn()

# Create energy predictor
compute_energy = energy_predictor(functional)

# Predict energy and Fock matrix
energy, fock = compute_energy(params, molecule)

Training

from grad_dft import train_kernel, loader
from optax import adam
from functools import partial
from jax import value_and_grad
import jax.numpy as jnp

# Load data with omegas
omegas = jnp.array([0.0, 0.3])
dataset = loader(fname="data.h5", config_omegas=omegas)

# Define loss
@partial(value_and_grad, has_aux=True)
def loss(params, molecule, true_energy):
    predicted_energy, fock = compute_energy(params, molecule)
    cost_value = (predicted_energy - true_energy) ** 2
    return cost_value, {"energy": predicted_energy}

# Train
tx = adam(learning_rate=1e-4)
kernel = jax.jit(train_kernel(tx, loss))

Project Structure

DeepRSH/
├── xc_functional.py       # RSH functional definition
├── train.py              # Training script
├── DeepsRSHXC.py         # GAT-based model
├── data_process.py       # Data preprocessing
├── GAT_Layer.py          # Graph Attention layers
├── nodes_embedding.py    # Node embeddings
├── bond_embedding.py     # Bond embeddings
└── dataset_diatoms/      # Diatomic molecule datasets

Range-Separated Parameters

The default omegas are [0.0, 0.3]:

  • ω = 0.0: Pure DFT (no exact exchange)
  • ω = 0.3: 30% long-range exact exchange (LC-ωPBE style)

Citation

If you use this code, please cite:

@software{deeprsh2024,
  title = {DeepRSH: Deep Learning Range-Separated Hybrid Functionals},
  author = {Your Name},
  year = {2024},
  url = {https://github.com/STOKES-DOT/DeepRSH}
}

License

MIT License