OMNI-P2x: A Universal Neural Network Potential for Excited-State Simulations

16 April 2025, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Photo-active molecular systems play an essential role in modern science and technology, finding applications in solar cells, organic light-emitting diodes (OLEDs), reaction catalysis, photodynamic therapy, and beyond. The rational design of photo-responsive molecules requires understanding of the photophysical and photochemical processes underlying their operation. This understanding can be gained via the first-principles quantum-mechanical (QM) calculations which, however, turn out prohibitively expensive for high-throughput investigations. To break through this limitation, here we introduce OMNI-P2x: the first universal neural network potential for molecular excited and ground electronic states. OMNI-P2x can be used, directly or after fine-tuning, in place of quantum-mechanical methods to perform a wide range of photophysical and photochemical simulations. OMNI-P2x is approaching the accuracy of time-dependent density functional theory (TD-DFT) methods at a fraction of the cost. Remarkably, this universal potential is more accurate and faster than established semiempirical QM methods, marking the watershed moment in theoretical method development for excited-state simulations. Here, we demonstrate its use in UV/Vis absorption spectroscopy, in real-time photodynamical simulations, and in the rational design of the visible-light-absorbing azobenzene systems.

Keywords

UV/Vis spectra
nonadiabatic dynamics
trajectory surface hopping
transfer learning
foundational model

Supplementary weblinks

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