A general method to describe intersystem crossing dynamics in trajectory surface hopping

Intersystem crossing is a radiationless process that can take place in a molecule irradiated by UV-Vis light, thereby playing an important role in many environmental, biological and technological processes. This paper reviews different methods to describe intersystem crossing dynamics, paying attention to semiclassical trajectory theories, which are especially interesting because they can be applied to large systems with many degrees of freedom. In particular, a general trajectory surface hopping methodology recently developed by the authors, which is able to include non-adiabatic and spin-orbit couplings in excited-state dynamics simulations, is explained in detail. This method, termed SHARC, can in principle include any arbitrary coupling, what makes it generally applicable to photophysical and photochemical problems, also those including explicit laser fields. A step-by-step derivation of the main equations of motion employed in surface hopping based on the fewest-switches method of Tully, adapted for the inclusion of spin-orbit interactions, is provided. Special emphasis is put on describing the different possible choices of the electronic bases in which spin-orbit can be included in surface hopping, highlighting the advantages and inconsistencies of the different approaches.Comment: 47 pages, 4 figure

Excited-State Dynamics in SO2: II. The Role of Triplet States in the Bound State Relaxation Studied by Surface-Hopping Simulations

The importance of triplet states in the photorelaxation dynamics of SO2 is studied by mixed quantum-classical dynamics simulations. Using the Surface Hopping including ARbitrary Couplings (Sharc) method, intersystem crossing processes caused by spin-orbit coupling are found occuring on an ultrafast time scale (few 100 fs) and thus competing with internal conversion. While in the singlet-only dynamics only oscillatory population transfer between the 1B1 and 1A2 states is observed, in the dynamics including singlet and triplet states we find additionally continuous ISC to the 3B2 state and to a smaller extent to the 3B1/3A2 coupled states. The populations obtained from the dynamics are discussed with respect to the overall nuclear motion and in the light of recent TRPEPICO studies [Wilkinson et al., paper I].Comment: 12 pages, 9 figure

Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra

Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for vibrational anharmonic and dynamical effects -- typically neglected by conventional quantum chemistry approaches -- we base our machine learning strategy on ab initio molecular dynamics simulations. While these simulations are usually extremely time consuming even for small molecules, we overcome these limitations by leveraging the power of a variety of machine learning techniques, not only accelerating simulations by several orders of magnitude, but also greatly extending the size of systems that can be treated. To this end, we develop a molecular dipole moment model based on environment dependent neural network charges and combine it with the neural network potentials of Behler and Parrinello. Contrary to the prevalent big data philosophy, we are able to obtain very accurate machine learning models for the prediction of infrared spectra based on only a few hundreds of electronic structure reference points. This is made possible through the introduction of a fully automated sampling scheme and the use of molecular forces during neural network potential training. We demonstrate the power of our machine learning approach by applying it to model the infrared spectra of a methanol molecule, n-alkanes containing up to 200 atoms and the protonated alanine tripeptide, which at the same time represents the first application of machine learning techniques to simulate the dynamics of a peptide. In all these case studies we find excellent agreement between the infrared spectra predicted via machine learning models and the respective theoretical and experimental spectra.Comment: 12 pages, 9 figure

Molecular Dynamics with Neural-Network Potentials

Molecular dynamics simulations are an important tool for describing the evolution of a chemical system with time. However, these simulations are inherently held back either by the prohibitive cost of accurate electronic structure theory computations or the limited accuracy of classical empirical force fields. Machine learning techniques can help to overcome these limitations by providing access to potential energies, forces and other molecular properties modeled directly after an electronic structure reference at only a fraction of the original computational cost. The present text discusses several practical aspects of conducting machine learning driven molecular dynamics simulations. First, we study the efficient selection of reference data points on the basis of an active learning inspired adaptive sampling scheme. This is followed by the analysis of a machine-learning based model for simulating molecular dipole moments in the framework of predicting infrared spectra via molecular dynamics simulations. Finally, we show that machine learning models can offer valuable aid in understanding chemical systems beyond a simple prediction of quantities

Ultrafast Intersystem Crossing in SO2_2 and Nucleobases

Mixed quantum-classical dynamics simulations show that intersystem crossing between singlet and triplet states in SO$_2$ and in nucleobases takes place on an ultrafast timescale (few 100~fs), directly competing with internal conversion.Comment: 4 pages, 2 figure

Photoelectron Spectra of 2-Thiouracil, 4-Thiouracil and 2,4-Dithiouracil

Ground- and excited-state UV photoelectron spectra of thiouracils (2-thiouracil, 4-thiouracil and 2,4-dithiouracil) have been simulated using multireference configuration interaction calculations and Dyson norms as measure for the photoionization intensity. Except for a constant shift, the calculated spectrum of 2-thiouracil agrees very well with experiment, while no experimental spectra are available for the two other compounds. For all three molecules, the photoelectron spectra show distinct bands due to ionization of the sulphur and oxygen lone pairs and the pyrimidine $\pi$ system. The excited-state photoelectron spectra of 2-thiouracil show bands at much lower energies than in the ground state spectrum, allowing to monitor the excited-state population in time-resolved UV photoelectron spectroscopy (TRPES) experiments. However, the results also reveal that single-photon ionization probe schemes alone will not allow monitoring all photodynamic processes existing in 2-thiouracil. Especially, due to overlapping bands of singlet and triplet states the clear observation of intersystem crossing will be hampered.Comment: 7 pages, 7 figure

Excitation of Nucleobases from a Computational Perspective II: Dynamics

This Chapter is devoted to unravel the relaxation processes taking place after photoexcitation of isolated DNA/RNA nucleobases in gas phase from a time-dependent perspective. To this aim, several methods are at hand, ranging from full quantum dynamics to various flavours of semiclassical or ab initio molecular dynamics, each with its advantages and its limitations. As this contribution shows, the most common approach employed up-to-date to learn about the deactivation of nucleobases in gas phase is a combination of the Tully surface hopping algorithm with on-the-fly CASSCF calculations. Different methods or, even more dramatically, different electronic structure methods can provide different dynamics. A comprehensive review of the different mechanisms suggested for each nucleobase is provided and compared to available experimental time scales. The results are discussed in a general context involving the effects of the different applied electronic structure and dynamics methods. Mechanistic similarities and differences between the two groups of nucleobases---the purine derivatives (adenine and guanine) and the pyrimidine derivatives (thymine, uracil, and cytosine)---are elucidated. Finally, a perspective on the future of dynamics simulations in the context of nucleobase relaxation is given.Comment: 54 pages, 19 figure

Photoinduced ultrafast dynamics and control of chemical reactions: from quantum to classical dynamics

This perspective on laser control provides a quick overview over different schemes for coherent control of chemical reactions and photophysical processes. It originally appeared in Bunsen Magazin 1, 13 - 23 (2012)

Machine learning and excited-state molecular dynamics

Machine learning is employed at an increasing rate in the research field of quantum chemistry. While the majority of approaches target the investigation of chemical systems in their electronic ground state, the inclusion of light into the processes leads to electronically excited states and gives rise to several new challenges. Here, we survey recent advances for excited-state dynamics based on machine learning. In doing so, we highlight successes, pitfalls, challenges and future avenues for machine learning approaches for light-induced molecular processes

Machine learning for electronically excited states of molecules

Electronically excited states of molecules are at the heart of photochemistry, photophysics, as well as photobiology and also play a role in material science. Their theoretical description requires highly accurate quantum chemical calculations, which are computationally expensive. In this review, we focus on how machine learning is employed not only to speed up such excited-state simulations but also how this branch of artificial intelligence can be used to advance this exciting research field in all its aspects. Discussed applications of machine learning for excited states include excited-state dynamics simulations, static calculations of absorption spectra, as well as many others. In order to put these studies into context, we discuss the promises and pitfalls of the involved machine learning techniques. Since the latter are mostly based on quantum chemistry calculations, we also provide a short introduction into excited-state electronic structure methods, approaches for nonadiabatic dynamics simulations and describe tricks and problems when using them in machine learning for excited states of molecules