ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2022
Simulations of molecular photodynamics in long timescales.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Efficient, Hierarchical, and Object-Oriented Electronic Structure Interfaces for Direct Nonadiabatic Dynamics Simulations.Journal of chemical theory and computation · 2025Article
- Surface Hopping Nested Instances Training Set for Excited-state Learning.Scientific data · 2025Article
- Selecting Initial Conditions for Trajectory-Based Nonadiabatic Simulations.Accounts of chemical research · 2025Article
- Perspective on Theoretical and Experimental Advances in Atmospheric Photochemistry.The journal of physical chemistry. A · 2024Review
- OH-Formation following vibrationally induced reaction dynamics of HPhysical chemistry chemical physics : PCCP · 2024Article
- MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows.Journal of chemical theory and computation · 2024Article
- Nonadiabatic Forward Flux Sampling for Excited-State Rare Events.Journal of chemical theory and computation · 2023Article
- Newton-X Platform: New Software Developments for Surface Hopping and Nuclear Ensembles.Journal of chemical theory and computation · 2022Article
- A Theoretical Perspective on the Actinic Photochemistry of 2-Hydroperoxypropanal.The journal of physical chemistry. A · 2022Article
- Chemistry without the Born-Oppenheimer approximation.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
Nonadiabatic dynamics simulations in the long timescale (much longer than 10 ps) are the next challenge in computational photochemistry. This paper delimits the scope of what we expect from methods to run such simulations: they should work in full nuclear dimensionality, be general enough to tackle any type of molecule and not require unrealistic computational resources. We examine the main methodological challenges we should venture to advance the field, including the computational costs of the electronic structure calculations, stability of the integration methods, accuracy of the nonadiabatic dynamics algorithms and software optimization. Based on simulations designed to shed light on each of these issues, we show how machine learning may be a crucial element for long time-scale dynamics, either as a surrogate for electronic structure calculations or aiding the parameterization of model Hamiltonians. We show that conventional methods for integrating classical equations should be adequate to extended simulations up to 1 ns and that surface hopping agrees semiquantitatively with wave packet propagation in the weak-coupling regime. We also describe our optimization of the Newton-X program to reduce computational overheads in data processing and storage. This article is part of the theme issue 'Chemistry without the Born-Oppenheimer approximation'.
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