arXiv subjects
Publications and source records attributed to Philipp Marquetand.
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).
In recent years, deep learning has become a part of our everyday life and is revolutionizing quantum chemistry as well. In this work, we show how deep learning can be used to advance the research field of photochemistry by learning all important properties for photodynamics simulations. The properties are multiple energies, forces, nonadiabatic couplings and spin-orbit couplings. The nonadiabatic couplings are learned in a phase-free manner as derivatives of a virtually constructed property by the deep learning model, which guarantees rotational covariance. Additionally, an approximation for nonadiabatic couplings is introduced, based on the potentials, their gradients and Hessians. As deep-learning method, we employ SchNet extended for multiple electronic states. In combination with the molecular dynamics program SHARC, our approach termed SchNarc is tested on a model system and two realistic polyatomic molecules and paves the way towards efficient photodynamics simulations of complex systems.
Excited-state dynamics simulations are a powerful tool to investigate photo-induced reactions of molecules and materials and provide complementary information to experiments. Since the applicability of these simulation techniques is limited by the costs of the underlying electronic structure calculations, we develop and assess different machine learning models for this task. The machine learning models are trained on {\emph ab initio} calculations for excited electronic states, using the methylenimmonium cation (CH$_2$NH$_2^+$) as a model system. For the prediction of excited-state properties, multiple outputs are desirable, which is straightforward with neural networks but less explored with kernel ridge regression. We overcome this challenge for kernel ridge regression in the case of energy predictions by encoding the electronic states explicitly in the inputs, in addition to the molecular representation. We adopt this strategy also for our neural networks for comparison. Such a state encoding enables not only kernel ridge regression with multiple outputs but leads also to more accurate machine learning models for state-specific properties. An important goal for excited-state machine learning models is their use in dynamics simulations, which needs not only state-specific information but also couplings, i.e., properties involving pairs of states. Accordingly, we investigate the performance of different models for such coupling elements. Furthermore, we explore how combining all properties in a single neural network affects the accuracy. As an ultimate test for our machine learning models, we carry out excited-state dynamics simulations based on the predicted energies, forces and couplings and, thus, show the scopes and possibilities of machine learning for the treatment of electronically excited states.
Photo-induced processes are fundamental in nature, but accurate simulations are seriously limited by the cost of the underlying quantum chemical calculations, hampering their application for long time scales. Here we introduce a method based on machine learning to overcome this bottleneck and enable accurate photodynamics on nanosecond time scales, which are otherwise out of reach with contemporary approaches. Instead of expensive quantum chemistry during molecular dynamics simulations, we use deep neural networks to learn the relationship between a molecular geometry and its high-dimensional electronic properties. As an example, the time evolution of the methylenimmonium cation for one nanosecond is used to demonstrate that machine learning algorithms can outperform standard excited-state molecular dynamics approaches in their computational efficiency while delivering the same accuracy.
The photodynamic properties of molecules determine their ability to survive in harsh radiation environments. As such, the photostability of heterocyclic aromatic compounds to electromagnetic radiation is expected to have been one of the selection pressures influencing the prebiotic chemistry on early Earth. In the present study, the gas-phase photodynamics of uracil, 5-methyluracil (thymine) and 2-thiouracil -- three heterocyclic compounds thought to be present during this era -- are assessed in the context of their recently proposed intersystem crossing pathways that compete with internal conversion to the ground state. Specifically, time-resolved photoelectron spectroscopy measurements evidence femtosecond to picosecond timescales for relaxation of the singlet 1$ππ$* and 1n$π$* states as well as for intersystem crossing to the triplet manifold. Trapping in the excited triplet state and intersystem crossing back to the ground state are investigated as potential factors contributing to the susceptibility of these molecules to ultraviolet photodamage.
The gas phase relaxation dynamics of photoexcited 5-azacytosine has been investigated by means of SHARC (surface-hopping including arbitrary couplings) molecular dynamics, based on accurate multireference electronic structure computations. Both singlet and triplet states were included in the simulations in order to investigate the different internal conversion and intersystem crossing pathways of this molecule. It was found that after excitation, 5-azacytosine undergoes ultrafast relaxation to the electronic ground state with a time constant of about 1~picosecond. Two important conical intersections have been identified as the funnel responsible for this deactivation mechanism. The very low intersystem crossing yield of 5-azacytosine has been explained by the size of the relevant spin-orbit coupling matrix elements, which are significantly smaller than in related molecules like cytosine or 6-azauracil. This difference is due to the fact that in 5-azacytosine the lowest singlet state is of $n_\mathrm{N}π^*$ nature, whereas in cytosine and 6-azauracil it is of $n_\mathrm{O}π^*$ character.
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.
We introduce weighted atom-centered symmetry functions (wACSFs) as descriptors of a chemical system's geometry for use in the prediction of chemical properties such as enthalpies or potential energies via machine learning. The wACSFs are based on conventional atom-centered symmetry functions (ACSFs) but overcome the undesirable scaling of the latter with increasing number of different elements in a chemical system. The performance of these two descriptors is compared using them as inputs in high-dimensional neural network potentials (HDNNPs), employing the molecular structures and associated enthalpies of the 133855 molecules containing up to five different elements reported in the QM9 database as reference data. A substantially smaller number of wACSFs than ACSFs is needed to obtain a comparable spatial resolution of the molecular structures. At the same time, this smaller set of wACSFs leads to significantly better generalization performance in the machine learning potential than the large set of conventional ACSFs. Furthermore, we show that the intrinsic parameters of the descriptors can in principle be optimized with a genetic algorithm in a highly automated manner. For the wACSFs employed here, we find however that using a simple empirical parametrization scheme is sufficient in order to obtain HDNNPs with high accuracy.
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.
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.
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.
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.
We study strong field molecular ionization using few- (four to ten) cycle laser pulses. Employing a supercontinuum light source, we are able to tune the optical laser wavelength (photon energy) over a range of about $\sim$200 nm (500 meV). We measure the photoelectron spectrum for a series of different molecules as a function of laser intensity, frequency, and bandwidth and illustrate how the ionization dynamics vary with these parameters. We find that multiphoton resonances and nonadiabatic dynamics (internal conversion) play an important role and result in ionization to different ionic continua. Interestingly, while nuclear dynamics can be "frozen" for sufficiently short laser pulses, we find that resonances strongly influence the photoelectron spectrum and final cationic state of the molecule regardless of pulse duration -- even for pulses that are less than four cycles in duration.
We study strong-field molecular ionization as a function of pulse duration. Experimental measurements of the photoelectron yield for a number of molecules reveal competition between different ionization continua (cationic states) which depends strongly on pulse duration. Surprisingly, in the limit of short pulse duration, we find that a single ionic continuum dominates the yield, whereas multiple continua are produced for longer pulses. Using calculations which take vibrational dynamics into account, we interpret our results in terms of nuclear motion and non-adiabatic dynamics during the ionization process.
An efficient perturbational treatment of spin-orbit coupling within the framework of high-level multi-reference techniques has been implemented in the most recent version of the COLUMBUS quantum chemistry package, extending the existing fully variational two-component (2c) multi-reference configuration interaction singles and doubles (MRCISD) method. The proposed scheme follows related implementations of quasi-degenerate perturbation theory (QDPT) model space techniques. Our model space is built either from uncontracted, large-scale scalar relativistic MRCISD wavefunctions or based on the scalar-relativistic solutions of the linear-response-theory-based multi-configurational averaged quadratic coupled cluster method (LRT-MRAQCC). The latter approach allows for a consistent, approximatively size-consistent and size-extensive treatment of spin-orbit coupling. The approach is described in detail and compared to a number of related techniques. The inherent accuracy of the QDPT approach is validated by comparing cuts of the potential energy surfaces of acrolein and its S, Se, and Te analoga with the corresponding data obtained from matching fully variational spin-orbit MRCISD calculations. The conceptual availability of approximate analytic gradients with respect to geometrical displacements is an attractive feature of the 2c-QDPT-MRCISD and 2c-QDPT-LRT-MRAQCC methods for structure optimization and ab inito molecular dynamics simulations.
We report ab initio excited-state dynamics simulations on isolated thymine to investigate the mechanism of intersystem crossing, based on CASSCF potential energy surfaces and the \textsc{Sharc} surface hopping method. We show that even though $S_2 \rightarrow S_1$ internal conversion is not described accurately with CASSCF, intersystem crossing can be correctly simulated. Intersystem crossing in thymine occurs from the $S_1$ ($^1nπ^*$) minimum, via a nearby crossing with $T_2$ ($^3ππ^*$). The system further relaxes via ultrafast internal conversion in the triplet manifold to the $T_1$ ($^3ππ^*$) state. The simulations reveal that, once the system is trapped in the $^1nπ^*$ minimum, intersystem crossing might proceed with a time constant of 1~ps. Furthermore, the change of the system's electronic state is accompanied respectively by elongation/shortening of specific bonds, which could thus be used as indicators to identify which state is populated in the dynamics.
Oxygen ($O_2$) is one of the most important elements required to sustain life. The concentration of $O_2$ on Earth has been accumulated over millions of years and has a direct connection with that of $CO_2$. Further, $CO_2$ plays an important role in many other planetary atmospheres. Therefore, molecular reactions involving $CO_2$ are critical for studying the atmospheres of such planets. Existing studies on the dissociation of $CO_2$ are exclusively focused on the C--O bond breakage. Here we report first experiments on the direct observation of molecular Oxygen formation from $CO_2$ in strong laser fields with a reaction microscope. Our accompanying simulations suggest that $CO_2$ molecules may undergo bending motion during and after strong-field ionization which supports the molecular Oxygen formation process. The observation of the molecular Oxygen formation from $CO_2$ may trigger further experimental and theoretical studies on such processes with laser pulses, and provide hints in studies of the $O_2$ and $CO_2^+$ abundance in $CO_2$-dominated planetary atmospheres.
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 $π$ 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.