Surface Hopping Advances Nonadiabatic Dynamics, Enabling Benchmarking of Photochemical Processes

Nonadiabatic molecular dynamics plays a crucial role in understanding the complex processes that govern photochemical and photophysical phenomena, and surface hopping methods represent a widely adopted approach within this field. Jakub Martinka, Mikołaj Martyka, and Biman Medhi, alongside Jiří Pittner and Pavlo O. Dral, have developed a flexible new framework within the MLatom package that significantly advances this area of research. Their work introduces a newly implemented Tully’s fewest-switches surface hopping algorithm, alongside its time-dependent Baeck, An variant, and demonstrates its capabilities through rigorous benchmarking and comparative analysis. This framework not only accelerates the development of new models for simulating molecular dynamics, but also provides researchers with powerful, easy-to-use tools for analysing both individual molecular trajectories and large ensembles, ultimately offering deeper insights into the intricacies of nonadiabatic dynamics.

Dral, have developed a flexible new framework within the MLatom package that significantly advances this area of research. Their work introduces a newly implemented Tully’s fewest-switches surface hopping algorithm, alongside its time-dependent Baeck, An variant, and demonstrates its capabilities through rigorous benchmarking and comparative analysis. This framework not only accelerates the development of new models for simulating molecular dynamics, but also provides researchers with powerful, easy-to-use tools for analysing both individual molecular trajectories and large ensembles, ultimately offering deeper insights into the intricacies of nonadiabatic dynamics.,.

Machine Learning Accelerates Nonadiabatic Molecular Dynamics Simulations

Scientists developed a flexible computational framework within the MLatom package to investigate nonadiabatic dynamics, a crucial area for understanding photochemical and photophysical processes. This work pioneers a newly implemented Tully’s fewest-switches surface hopping algorithm, alongside its time-dependent Baeck–An variant, expanding MLatom’s capabilities for simulating complex molecular interactions. The study meticulously addresses the challenges of applying machine learning to nonadiabatic molecular dynamics, focusing on accurately predicting energies, gradients, and nonadiabatic couplings, essential components for simulating excited-state behavior. Researchers engineered methods to efficiently obtain energies, energy gradients, and nonadiabatic couplings, demonstrating that user-defined custom models can significantly reduce computational time while facilitating benchmarking of machine learning models.

The team specifically tackled the complexities of training machine learning models to predict nonadiabatic couplings, which are vectorial quantities that diverge near conical intersections and are sensitive to the arbitrary phase of wavefunctions. To address these issues, scientists employed active learning strategies to carefully assemble training datasets, ensuring accurate representation of the potential energy surfaces and facilitating reliable predictions of excited-state deactivation processes. Experiments employed a comparative analysis of curvature-driven surface hopping schemes, revealing that the Landau–Zener approach outperforms the time-dependent Baeck–An scheme in simulating nonadiabatic transitions. The study also showcases easy-to-use analysis tools for both individual molecular trajectories and ensembles of trajectories, enabling detailed investigation of population transfer between electronic states and associated configurational changes. This framework enables accelerated development of machine learning models and provides deeper insight into nonadiabatic dynamics, offering a powerful tool for exploring complex chemical processes.,.

Machine Learning Accelerates Molecular Dynamics Simulations

The development of accurate and efficient methods for simulating the behavior of molecules during photochemical and photophysical processes represents a significant advancement in computational chemistry. Researchers have implemented a flexible framework within the MLatom package, incorporating Tully’s fewest-switches surface hopping algorithm and its time-dependent Baeck, An variant, to investigate these nonadiabatic dynamics. This work demonstrates the power of combining robust machine learning protocols with established simulation techniques, providing a versatile Python API for studying complex molecular processes. The team focused on optimizing methods for calculating energy, energy gradients, and nonadiabatic couplings, demonstrating that user-defined custom models can substantially reduce computational time while facilitating benchmarking of different approaches.

Comparative studies of curvature-driven surface hopping schemes revealed that the Landau, Zener approach outperforms the time-dependent Baeck, An scheme in simulating molecular dynamics. These findings are crucial for selecting the most appropriate method for specific research questions and computational resources. Furthermore, the researchers developed easy-to-use analysis tools for both individual molecular trajectories and ensembles of trajectories, enabling deeper insights into nonadiabatic dynamics. The framework supports interfaces to a wide range of quantum chemistry programs, including COLUMBUS, MOLCAS, and Gaussian, allowing simulations with methods such as complete active space self-consistent field and time-dependent density functional theory.

It also integrates a broad library of machine learning models, including DPMD, ANI, and MS-ANI, expanding the versatility of the framework. The implemented fewest-switches surface hopping method propagates the system on potential energy surfaces by solving classical equations of motion, while coefficients describing the system’s state are updated using a locally approximated time-dependent Schrödinger equation. The team implemented a simplified decay of mixing correction to account for decoherence, ensuring accurate simulations. To conserve total energy during hops between electronic states, velocities are rescaled, allowing for accurate modeling of energy transfer. Importantly, the framework also includes NAC-free surface hopping schemes, such as the Landau, Zener approach, which are valuable when nonadiabatic couplings are unavailable. These advancements deliver accelerated model development and provide deeper understanding of nonadiabatic dynamics, paving the way for future integration with AI agents for autonomous simulations.,.

Nonadiabatic Dynamics Framework Validated for Molecular Simulations

This work presents a new computational framework within the MLatom package for simulating nonadiabatic molecular dynamics, a crucial technique for understanding photochemical and photophysical processes. Researchers successfully implemented and validated Tully’s fewest-switches surface hopping algorithm, alongside its time-dependent Baeck, An variant, demonstrating the framework’s flexibility through applications to fulvene, a molecular ferro-wire, and the methylenimmonium cation. Comparative studies of different surface hopping schemes reveal that the Landau, Zener approach generally outperforms the time-dependent Baeck, An scheme, particularly in systems where accurate representation of transition dynamics is essential. The developed framework also incorporates user-friendly analysis tools for both individual molecular trajectories and larger ensembles, enabling detailed insights into nonadiabatic processes and facilitating comparisons between different computational methods.

Analysis of the test systems demonstrates the framework’s ability to accurately model energy transfer and relaxation pathways, while the integrated tools allow for efficient population analysis and identification of key structural changes during hopping events. The authors acknowledge limitations in the current implementation, specifically the absence of controls for transition density coupling values and a blocking algorithm to prevent spurious hops, which could be addressed in future work. This advancement promises to accelerate the development of more accurate and efficient models for simulating complex photochemical reactions and provides researchers with a powerful tool for exploring the dynamics of nonadiabatic processes.