[ICLR 2026] NextHAM: Advancing Universal Deep Learning for Electronic-Structure Hamiltonian Prediction of Materials

This is the official implementation of the paper "Advancing Universal Deep Learning for Electronic-Structure Hamiltonian Prediction of Materials", accepted at ICLR 2026.

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⚙️ Requirements

Hardware

Both training and inference were conducted on high-performance hardware:

• GPU: 4x NVIDIA A800 (80GiB VRAM each).
• Note: Due to the high dimensionality of Hamiltonian matrices, a large GPU memory is recommended for optimal performance.

Software Environment

The software dependencies are managed via Conda. All required packages are specified in the ./environment.yml file.

To set up the environment, run:

# Create the environment from the file
conda env create -f environment.yml

# Activate the environment
conda activate nextham

📊 Dataset Preparation

The Materials-SOC-HAM dataset proposed in this paper is publicly available.

1. Download: You can access the dataset via the link below:

   • Link: Materials-SOC-HAM Dataset
   • Extraction Code: DGEo

2. Configuration: Once the dataset is downloaded and extracted to your local directory, you must update the file roots:

   • Locate the following files: datasets/train.txt, datasets/val.txt, and datasets/test.txt.
   • Replace the placeholder /your_path/ in each file with the absolute path to your local dataset directory.

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🚀 Training and Evaluation

1. Pre-trained Models (Optional)

   If you wish to train on new data, we highly recommend fine-tuning from our provided pre-trained models to achieve faster convergence.

   • Download Link: NextHAM Pre-trained Weights
   • Password: QoYA
   • Setup: Please download the weights and place them in the ./pretrained_models directory.

2. Training To start the model training and validation process, execute the following script:

   sh scripts/train/train_val.sh

3. Testing To test the model after training, execute the following script:

   sh scripts/test/test.sh

📝 Citations

@inproceedings{yin2026nextham,
title={Advancing Universal Deep Learning for Electronic-Structure Hamiltonian Prediction of Materials},
author={Shi Yin, Zujian Dai, Xinyang Pan, and Lixin He},
booktitle={International Conference on Learning Representations (ICLR)},
year={2026}
}