Dynamical Insights into the Decomposition of 1,2-Dioxetane

Dissociation Time, Quantum Yield, and Dynamic Reaction Pathways in the Thermolysis of trans-3,4-Dimethyl-1,2-dioxetane

The thermolysis of trans-3,4-dimethyl-1,2-dioxetane is studied by trajectory surface hopping. The significant difference between long and short dissociation times is rationalized by frustrated dissociations and the time spent in triplet states. If the C-C bond breaks through an excited state channel, then the trajectory passes over a ridge of the potential energy surface of that state. The calculated triplet quantum yields match the experimental results. The dissociation half-times and quantum yields follow the same ascending order as per the product states, justifying the conjecture that the longer dissociation time leads to a higher quantum yield, proposed in the context of the methylation effect. The populations of the molecular Coulomb Hamiltonian and diagonal states reach equilibrium, but the triplet populations with different Sz components fluctuate indefinitely. Certain initial velocities, leading the trajectories to given product states, can be identified as the most characteristic features for sorting trajectories according to their product states.

From a one-mode to a multi-mode understanding of conical intersection mediated ultrafast organic photochemical reactions

This review discusses how ultrafast organic photochemical reactions are controlled by conical intersections, highlighting that decay to the ground-state at multiple points of the intersection space results in their multi-mode character.

Conical Intersection in Chemiluminescence of Cyclic Peroxides

Chemiluminescence (CL) utilizing chemiexcitation for energy transformation is one of the most highly sensitive and useful analytical techniques. The chemiexcitation is a chemical process of a ground-state reactant producing an excited-state product, in which a nonadiabatic event is facilitated by conical intersections (CIs), the specific molecular geometries where electronic states are degenerated. Cyclic peroxides, especially 1,2-dioxetane/dioxetanone derivatives, are the iconic chemiluminescent substances. In this Perspective, we concentrated on the CIs in the CL of cyclic peroxides. We first present a computational overview on the role of CIs between the ground (S₀) state and the lowest singlet excited (S₁) state in the thermolysis of cyclic peroxides. Subsequently, we discuss the role of the S₀/S₁ CI in the CL efficiency and point out misunderstandings in some theoretical studies on the singlet chemiexcitations of cyclic peroxides. Finally, we address the challenges and future prospects in theoretically calculating S₀/S₁ CIs and simulating the dynamics and chemiexcitation efficiency in the CL of cyclic peroxides.

Role of conical intersection seam topography in the chemiexcitation of 1,2-dioxetanes

Chemiexcitation, the generation of electronic excited states by a thermal reaction initiated on the ground state, is an essential step in chemiluminescence, and it is mediated by the presence of a conical intersection that allows a nonadiabatic transition from ground state to excited state. Conical intersections classified as sloped favor chemiexcitation over ground state relaxation. The chemiexcitation yield of 1,2-dioxetanes is known to increase upon methylation. In this work we explore to which extent this trend can be attributed to changes in the conical intersection topography or accessibility. Since conical intersections are not isolated points, but continuous seams, we locate regions of the conical intersection seams that are close to the configuration space traversed by the molecules as they react on the ground state. We find that conical intersections are energetically and geometrically accessible from the reaction trajectory, and that topographies favorable to chemiexcitation are found in all three molecules studied. Nevertheless, the results suggest that dynamic effects are more important for explaining the different yields than the static features of the potential energy surfaces.

Entropic Path Sampling: Computational Protocol to Evaluate Entropic Profile along a Reaction Path

Fleeting intermediates constitute dynamically stepwise mechanisms. They have been characterized in molecular dynamics trajectories, but whether these intermediates form a free energy minimum to become entropic intermediates remains elusively defined. We developed a computational protocol known as entropic path sampling to evaluate the entropic variation of reacting species along a reaction path based on an ensemble of trajectories. Using cyclopentadiene dimerization as a model reaction, we observed an entropy maximum along the reaction path which originates from an enhanced conformational flexibility as the reacting species enter into a flat energy region. As the reacting species further approach product formation, unfavorable entropic restriction fails to offset the potential energy drop, resulting in no free energy minimum along the post-TS pathway. Our results show that cyclopentadiene dimerization involves an entropy maximum that leads to dynamic intermediates with elongated lifetimes, but the reaction does not involve entropic intermediates.

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 not only how machine learning is employed 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 and approaches for nonadiabatic dynamics simulations and describe tricks and problems when using them in machine learning for excited states of molecules.

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 not only how machine learning is employed 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 and approaches for nonadiabatic dynamics simulations and describe tricks and problems when using them in machine learning for excited states of molecules.

The role of CO2 detachment in fungal bioluminescence: thermally vs. excited state induced pathways

Different fungi lineages are known to emit light on Earth, mainly in tropical climates. Although the preparation of bioluminescent cell-free extracts allowed one to characterize the enzymatic requirements, the molecular mechanism underlying luminescence is still largely unknown and is based on the experimental putative assumption that a high-energy intermediate should be formed by reaction with O2 and formation of an endoperoxide. Here, we aim at determining, through state-of-the-art multiconfigurational quantum chemistry, the full mechanistic landscape leading from the endoperoxide to the emitting species, envisaging different possible pathways and proposing their viability. Especially, thermal CO2 detachment followed by excited-state peroxide opening (thermal-chemiluminescence) can compete with a parallel pathway, i.e., first excited-state endoperoxide opening, followed by CO2 detachment on the same excited-state (excited state-chemiluminescence). Clear differences in the energy supplies, as well as the possibility to directly populate the emitting species from the intersection seam between ground and excited states, land credence to a kinetically efficient thermal-chemiluminescent pathway, establishing for the first time a detailed description of fungal bioluminescence.

Modern quantum chemistry with [Open]Molcas

MOLCAS/OpenMolcas is an ab initio electronic structure program providing a large set of computational methods from Hartree-Fock and density functional theory to various implementations of multiconfigurational theory. This article provides a comprehensive overview of the main features of the code, specifically reviewing the use of the code in previously reported chemical applications as well as more recent applications including the calculation of magnetic properties from optimized density matrix renormalization group wave functions.

Quantum yields of singlet and triplet chemiexcitation of dimethyl 1,2-dioxetane: ab initio nonadiabatic molecular dynamic simulations

The global nonadiabatic switching on-the-fly trajectory surface hopping simulation at the 8SA-CASSCF quantum level has been performed to estimate the quantum yield of chemiexcitation for the uncatalyzed decomposition reaction of the open-shell biradical trans-3,4-dimethyl-1,2-dioxetane system. The present ab initio nonadiabatic molecular dynamic simulation involving both conical intersection and intersystem crossing is to compute for the first time the population evolution of quantum yields at the four lowest singlet and four lowest triplet states. The simulated results demonstrate not only the stepwise dissociation of O-O and C-C bond breaking, but also confirm the existence of a biradical entropic trap which is responsible for chemiexcitation. The simulated quantum yield of the triplet chemiexcitation ΦT1 = 0.266 ± 0.096 agrees with the experimental value of 0.20 ± 0.04 very well. The present nonadiabatic molecular dynamic simulation of dimethyl 1,2-dioxetanes provides a further advanced understanding and stepping stone for future studies on chemi- and bio-luminescence.

Oxidative degradation pathways of beclabuvir hydrochloride mediated by hydrogen peroxide and UV/Vis light

Beclabuvir hydrochloride (BCV) is a marketed product for the treatment of hepatitis C viral infection. It contains functional groups such as indole, tertiary amine and amides. Forced degradation studies of BCV revealed different degradation profiles under photo and hydrogen peroxide oxidative conditions. Under the photo-oxidative degradation conditions, the tertiary amine on the piperazine ring was oxidized to form the degradants as the hydroxyl and des-methyl analogs of beclabuvir. However, under the oxidative condition using hydrogen peroxide, the indole ring was oxidized to form a typical kynuric degradant and two unexpected cyclohexyl rearranged diastereomeric degradants. The plausible mechanisms for the photo and hydrogen peroxide mediated oxidative degradation of beclabuvir hydrochloride are proposed.

How machine learning can assist the interpretation of ab initio molecular dynamics simulations and conceptual understanding of chemistry

Molecular dynamics simulations are often key to the understanding of the mechanism, rate and yield of chemical reactions. One current challenge is the in-depth analysis of the large amount of data produced by the simulations, in order to produce valuable insight and general trends. In the present study, we propose to employ recent machine learning analysis tools to extract relevant information from simulation data without a priori knowledge on chemical reactions. This is demonstrated by training machine learning models to predict directly a specific outcome quantity of ab initio molecular dynamics simulations - the timescale of the decomposition of 1,2-dioxetane. The machine learning models accurately reproduce the dissociation time of the compound. Keeping the aim of gaining physical insight, it is demonstrated that, in order to make accurate predictions, the models evidence empirical rules that are, today, part of the common chemical knowledge. This opens the way for conceptual breakthroughs in chemistry where machine analysis would provide a source of inspiration to humans.

Chemi- and Bioluminescence of Cyclic Peroxides

Bioluminescence is a phenomenon that has fascinated mankind for centuries. Today the phenomenon and its sibling, chemiluminescence, have impacted society with a number of useful applications in fields like analytical chemistry and medicine, just to mention two. In this review, a molecular-orbital perspective is adopted to explain the chemistry behind chemiexcitation in both chemi- and bioluminescence. First, the uncatalyzed thermal dissociation of 1,2-dioxetane is presented and analyzed to explain, for example, the preference for triplet excited product states and increased yield with larger nonreactive substituents. The catalyzed fragmentation reaction and related details are then exemplified with substituted 1,2-dioxetanone species. In particular, the preference for singlet excited product states in that case is explained. The review also examines the diversity of specific solutions both in Nature and in artificial systems and the difficulties in identifying the emitting species and unraveling the color modulation process. The related subject of excited-state chemistry without light absorption is finally discussed. The content of this review should be an inspiration to human design of new molecular systems expressing unique light-emitting properties. An appendix describing the state-of-the-art experimental and theoretical methods used to study the phenomena serves as a complement.

How Do Methyl Groups Enhance the Triplet Chemiexcitation Yield of Dioxetane?

Chemiluminescence is the emission of light as a result of a nonadiabatic chemical reaction. The present work is concerned with understanding the yield of chemiluminescence, in particular how it dramatically increases upon methylation of 1,2-dioxetane. Both ground-state and nonadiabatic dynamics (including singlet excited states) of the decomposition reaction of various methyl-substituted dioxetanes have been simulated. Methyl-substitution leads to a significant increase in the dissociation time scale. The rotation around the O-C-C-O dihedral angle is slowed; thus, the molecular system stays longer in the "entropic trap" region. A simple kinetic model is proposed to explain how this leads to a higher chemiluminescence yield. These results have important implications for the design of efficient chemiluminescent systems in medical, environmental, and industrial applications.