# Physics-informed Hamiltonian learning for large-scale optoelectronic property prediction

## TL;DR

The paper introduces HAMSTER, a physics-informed machine learning framework that accurately predicts optoelectronic properties of large chemical systems with fewer calculations.

## Contribution

HAMSTER combines physical principles with machine learning to efficiently and accurately predict Hamiltonians in large-scale systems.

## Key findings

- HAMSTER achieves accurate optoelectronic property prediction in halide perovskites across temperature and composition changes.
- The framework scales to systems with tens of thousands of atoms using minimal first-principles calculations.
- It maintains physical interpretability while improving data efficiency and accuracy over traditional models.

## Abstract

Predicting optoelectronic properties of large-scale atomistic systems under realistic conditions is crucial for rational materials design, yet computationally prohibitive with first-principles simulations. Recent neural network models have shown promise in overcoming these challenges, but typically require large datasets and lack physical interpretability. Physics-inspired approximate models offer greater data efficiency and intuitive understanding, but often sacrifice accuracy and transferability. Here we present HAMSTER, a physics-informed machine learning framework for predicting the quantum-mechanical Hamiltonian of complex chemical systems. Starting from an approximate model encoding essential physical effects, HAMSTER captures the critical influence of dynamic environments on Hamiltonians using only few explicit first-principles calculations. We demonstrate our approach on halide perovskites, achieving accurate prediction of optoelectronic properties across temperature and compositional variations, and scalability to systems containing tens of thousands of atoms. This work highlights the power of physics-informed Hamiltonian learning for accurate and interpretable optoelectronic property prediction in large, complex systems.

Schwade et al. report HAMSTER, a physics-informed machine learning framework for predicting the quantum-mechanical Hamiltonian of complex chemical systems. It yields accurate property prediction of halide perovskites across various temperatures and compositions for systems containing 50,000 atoms.