PYSEQM 2.0: Accelerated Semiempirical Excited-State Calculations on Graphical Processing Units

We report the development and implementation of electronic excited-state capabilities for semiempirical quantum chemical methods at both the Configuration Interaction Singles and Time-Dependent Hartree–Fock levels of theory, integrated within the PYSEQM 2.0 software package (https://github.com/lanl/PYSEQM). PYSEQM is a Python-based package designed for efficient and scalable quantum chemical simulations. Leveraging the PyTorch framework enables PYSEQM to benefit from automatic differentiation and GPU acceleration, leading to substantial performance gains in molecular property evaluations. In particular, our implementation enables efficient calculation of excited-state properties for large molecular systems. Benchmarking on systems with up to a thousand atoms demonstrates that excited-state computations can be completed in under a minute on modern GPUs, making this approach particularly suitable for high-throughput screening, real-time feedback in interactive simulations, and large-scale dynamical studies. Additionally, PYSEQM includes a machine learning interface that supports Hamiltonian parameter reoptimization and neural network training. These capabilities open new avenues for data-driven excited-state dynamics simulations, offering a path toward combining quantum chemical rigor with machine learning efficiency. Overall, this work facilitates access to excited-state quantum chemistry for large systems, while laying the foundation for future hybrid quantum-machine-learning approaches in photochemistry, photophysics, and materials discovery.