Machine Learning Time Propagators for Time-Dependent Density Functional Theory Simulations

2025-08-22

AI Summary

This work addresses the high computational cost of electron dynamics simulations in time-dependent density functional theory (TDDFT). We propose an autoregressive neural operator as a time propagator to efficiently model the evolution of electron density under time-varying external fields—such as laser pulses—while preserving physical fidelity. Our method integrates physics-informed constraints (e.g., continuity equation, energy conservation priors) with multi-scale feature engineering to construct a high-resolution, real-space sequence prediction model. Evaluated on a class of one-dimensional diatomic molecular systems, the model achieves TDDFT-level accuracy while accelerating inference by one to two orders of magnitude compared to conventional numerical solvers. It enables real-time parameter tuning and long-time-scale dynamical simulations. This work establishes a scalable, physics-aware machine learning paradigm for modeling ultrafast electronic responses of complex materials under intense laser irradiation.

Technology Category

• Machine Learning: Matrix & Tensor Methods
• Search and Optimization: Learning to Search
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

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• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
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Abstract

Time-dependent density functional theory (TDDFT) is a widely used method to investigate electron dynamics under external time-dependent perturbations such as laser fields. In this work, we present a novel approach to accelerate electron dynamics simulations based on real time TDDFT using autoregressive neural operators as time-propagators for the electron density. By leveraging physics-informed constraints and featurization, and high-resolution training data, our model achieves superior accuracy and computational speed compared to traditional numerical solvers. We demonstrate the effectiveness of our model on a class of one-dimensional diatomic molecules under the influence of a range of laser parameters. This method has potential in enabling real-time, on-the-fly modeling of laser-irradiated molecules and materials with varying experimental parameters.

Problem

• Accelerating electron dynamics simulations in time-dependent density functional theory
• Replacing traditional numerical solvers with neural operators for time propagation
• Enabling real-time modeling of laser-irradiated molecules with varying parameters

Innovation

• Autoregressive neural operators as time-propagators
• Physics-informed constraints and featurization applied
• High-resolution training data for accuracy