NextHAM: Inference Pipeline

This repository is a lightweight inference branch for the paper [[ICLR 2026] NextHAM: Advancing Universal Deep Learning for Electronic-Structure Hamiltonian Prediction of Materials].

Unlike the main branch (which includes full training, testing, and ground-truth accuracy comparisons), this branch focuses strictly on the inference pipeline. It is heavily simplified, runs much faster, and is specifically designed for predicting the Hamiltonian of new material structures.

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⚙️ Environment & Compilation Requirements

Python Environment

The Python dependencies are identical to the main repository. Please refer to the NextHAM Main Branch for detailed Conda/Pip installation instructions.

🚀 Quick Start & Usage

0. Pre-requisite: Generate Zeroth-Step Hamiltonian

Before running the pipeline, you need to generate the zeroth-step Hamiltonian using the get_hs code.

• Source Code: abacus-develop/largescale
• Compile and run this code on your target material samples. In our examples, we use a Silicon (Si) system as a sample. You can download the example folder from here (Extraction code: bFeV) and place it in the get_hs_res/si/ directory.

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🏃‍♂️ Running the Pipeline

Script: full_inference_pipeline_python.sh

sh full_inference_pipeline_python.sh

What this script does:

1. Runs pre_process.py to parse ABACUS outputs and generate the .pth graph entirely in Python.
2. Combines the data and runs inference (infer.sh).
3. Post-processes the predicted tensors and plots the band structure using post_process.py.

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🔍 Customizing the Scripts for Your Materials

If you are evaluating your own structures, open either .sh script and modify the variables at the top:

# Modify these paths to point to your specific structure directories
TARGET_DIR="${BASE_DIR}/get_hs_res/YOUR_MATERIAL_DIR"

# Modify the Fermi energy for your specific system (crucial for accurate band plotting)
FERMI_ENERGY="6.58"

The output plots and final matrices will be saved in the res_si_split/plots/ (or your defined --save-path) directory.

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For full training pipelines, evaluation, and accuracy comparisons against ground truth, please visit the NextHAM Main Repository.