ByteDance-Seed/SCFbench

👋 Hi, everyone!
We are ByteDance Seed team.

You can get to know us better through the following channels👇

Towards A Universally Transferable Acceleration Method for Density Functional Theory

Zhe Liu, Yuyan Ni, Zhichen Pu, Qiming Sun, Siyuan Liu & Wen Yan

https://arxiv.org/abs/2509.25724

TL;DR

We propose a framework for accelerating DFT calculations.

We train E(3)-equivariant neural networks to predict the expansion coefficients of the electron density in an auxiliary basis, and use the prediction to construct an initial guess for the SCF process. This approach exhibits superior transferability in various aspects.

Contents

The repo currently contains the following contents:

• The full SCFbench dataset.
• The data pipeline for the SCFbench dataset.
• The PyTorch nn.Module of the species-wise linear layer for the prediction of the electron density coefficients.
• The NequIP model architecture with the species-wise linear layer.
• Example code for computing the density coefficients from a density matrix.

We will also release the following items soon:

• The training code for models.
• The full evaluation code.

Requirements

• torch
• e3nn
• pyscf
• lmdb
• numpy>1.26
• nequip (if you want to use the NequIP model)

Dataset Usage

The sample dataset contains the main dataset (the dataset for training, validation and in-distribution testing) and the ood-test dataset.

Each dataset contains several parts, each of which corresponds to a specific piece of information. The parts are:

• base: the basic information of the molecule, including atomic numbers, coordinates, etc.
• dm: the density matrix of the molecule.
• fock: the Hamiltonian (fock) matrix of the molecule.
• auxdensity.denfit: the density coefficients on def2-universal-jfit.
• auxdensity.denfit.etb2.0: the density coefficients on the ETB basis of def2-svp with $\beta=2.0$.
• auxdensity.denfit.etb1.5: the density coefficients on the ETB basis of def2-svp with $\beta=1.5$.

Example:

from dataset import SCFBenchDataset

# Loading base info (atomic numbers, coordinates, etc.), density matrix, Hamiltonian (fock) matrix and the density coefficients on def2-universal-jfit.
parts_to_load = ['base', 'dm', 'fock', 'auxdensity.denfit']
dataset = SCFBenchDataset(data_root='dataset/main', parts_to_load=parts_to_load)
dataset[0].keys()

# Loading the base info and the density coefficients on the ETB basis of def2-svp with $\beta=1.5$.
parts_to_load = ['base', 'auxdensity.denfit.etb1.5']
dataset = SCFBenchDataset(data_root='dataset/ood-test', parts_to_load=parts_to_load, auxbasis='etb:def2-svp:1.5') 
dataset[0].keys()

# for the raw data, use the underlying dataset
dataset.dataset[0].keys()

Citing SCFbench

If you use SCFbench in your research, please cite:

@misc{liu2025universallytransferableaccelerationmethod,
      title={Towards A Universally Transferable Acceleration Method for Density Functional Theory}, 
      author={Zhe Liu and Yuyan Ni and Zhichen Pu and Qiming Sun and Siyuan Liu and Wen Yan},
      year={2025},
      eprint={2509.25724},
      archivePrefix={arXiv},
      primaryClass={physics.chem-ph},
      url={https://arxiv.org/abs/2509.25724}, 
}

License

Models are licensed under the Apache License, Version 2.0.

The dataset is a derivative of ChEMBL, used under CC BY-SA 3.0.

Our modified version, the SCFbench dataset, is also licensed under CC BY-SA 3.0.

About ByteDance Seed Team

Founded in 2023, ByteDance Seed Team is dedicated to crafting the industry's most advanced AI foundation models. The team aspires to become a world-class research team and make significant contributions to the advancement of science and society.