Transferability of atomic energies from alchemical decomposition.

We study alchemical atomic energy partitioning as a method to estimate atomization energies from atomic contributions, which are defined in physically rigorous and general ways through the use of the uniform electron gas as a joint reference. We analyze quantitatively the relation between atomic energies and their local environment using a dataset of 1325 organic molecules. The atomic energies are transferable across various molecules, enabling the prediction of atomization energies with a mean absolute error of 23 kcal/mol, comparable to simple statistical estimates but potentially more robust given their grounding in the physics-based decomposition scheme. A comparative analysis with other decomposition methods highlights its sensitivity to electrostatic variations, underlining its potential as a representation of the environment as well as in studying processes like diffusion in solids characterized by significant electrostatic shifts.

Nonadiabatic Forward Flux Sampling for Excited-State Rare Events.

We present a rare event sampling scheme applicable to coupled electronic excited states. In particular, we extend the forward flux sampling (FFS) method for rare event sampling to a nonadiabatic version (NAFFS) that uses the trajectory surface hopping (TSH) method for nonadiabatic dynamics. NAFFS is applied to two dynamically relevant excited-state models that feature an avoided crossing and a conical intersection with tunable parameters. We investigate how nonadiabatic couplings, temperature, and reaction barriers affect transition rate constants in regimes that cannot be otherwise obtained with plain, traditional TSH. The comparison with reference brute-force TSH simulations for limiting cases of rareness shows that NAFFS can be several orders of magnitude cheaper than conventional TSH and thus represents a conceptually novel tool to extend excited-state dynamics to time scales that are able to capture rare nonadiabatic events.

Long-Lived Electronic Coherences in Molecules.

We demonstrate long-lived electronic coherences in molecules using a combination of measurements with shaped octave spanning ultrafast laser pulses and calculations of the light matter interaction. Our pump-probe measurements prepare and interrogate entangled nuclear-electronic wave packets whose electronic phase remains well defined despite vibrational motion along many degrees of freedom. The experiments and calculations illustrate how coherences between excited states can survive, even when coherence with the ground state is lost, and may have important implications for many areas of attosecond science and photochemistry.

Arene Variation of Highly Cytotoxic Tridentate Naphthoquinone-Based Ruthenium(II) Complexes and In-Depth In Vitro Studies.

The main purpose of this study was to synthesize a new set of naphthoquinone-based ruthenium(II) arene complexes and to develop an understanding of their mode of action. This study systematically reviews the steps of synthesis, aiming to provide a simplified approach using microwave irradiation. The chemical structures and the physicochemical properties of this novel group of compounds were examined by 1H-NMR and 13C-NMR spectroscopy, X-ray diffractometry, HPLC-MS and supporting DFT calculations. Several aspects of the biological activity were investigated in vitro, including short- and long-term cytotoxicity tests, cellular accumulation studies, detection of reactive oxygen species generation, apoptosis induction and NAD(P)H:quinone oxidoreductase 1 (NQO1) activity as well as cell cycle analysis in A549, CH1/PA-1, and SW480 cancer cells. Furthermore, the DNA interaction ability was studied in a cell-free assay. A positive correlation was found between cytotoxicity, lipophilicity and cellular accumulation of the tested complexes, and the results offer some important insights into the effects of the arene. The most obvious finding to emerge from this study is that the usually very chemosensitive CH1/PA-1 teratocarcinoma cells showed resistance to these phthiocol-based organometallics in comparison to the usually less chemosensitive SW480 colon carcinoma cells, which pilot experiments suggest as being related to NQO1 activity.

Luminescent Iridium Complexes with a Sulfurated Bipyridine Ligand: PCET Thermochemistry of the Disulfide Unit and Photophysical Properties.

Molecular systems combining light harvesting and charge storage are receiving great attention in the context of, for example, artificial photosynthesis and solar fuel generation. As part of ongoing efforts to develop new concepts for photoinduced proton-coupled electron transfer (PCET) reactivities, we report a cyclometallated iridium(III) complex [Ir(ppy)2(S-Sbpy)](PF6) ([1]PF6) equipped with our previously developed sulfurated bipyridine ligand S-Sbpy. A new one-step synthetic protocol for S-Sbpy is developed, starting from commercially available 2,2'-bipyridine, which significantly facilitates the use of this ligand. [1]+ features a two-electron reduction with potential inversion (|E1| > |E2|) at moderate potentials (E1 = -1.12, E2 = -1.11 V versus. Fc+/0 at 253 K), leading to a dithiolate species [1]-. Protonation with weak acids allows for determination of pKa = 23.5 in MeCN for the S-H···S- unit of [1H]. The driving forces for both the H atom and the hydride transfer are calculated to be ∼60 kcal mol-1 and verified experimentally by reaction with a suitable H atom and a hydride acceptor, demonstrating the ability of [1]+ to serve as a versatile PCET reagent, albeit with limited thermal stability. In MeCN solution, an orange emission for [1]PF6 from a triplet-excited state was found. Density functional calculations and ultrafast absorption spectroscopy are used to give insight into the excited-state dynamics of the complex and suggest a significantly stretched S-S bond for the lowest triplet-state T1. The structural responsiveness of the disulfide unit is proposed to open an effective relaxation channel toward the ground state, explaining the unexpectedly short lifetime of [1]+. These insights as well as the quantitative ground-state thermochemistry data provide valuable information for the use of S-Sbpy-functionalized complexes and their disulfide-/dithiol-directed PCET reactivity.

Deep learning study of tyrosine reveals that roaming can lead to photodamage.

Amino acids are among the building blocks of life, forming peptides and proteins, and have been carefully 'selected' to prevent harmful reactions caused by light. To prevent photodamage, molecules relax from electronic excited states to the ground state faster than the harmful reactions can occur; however, such photochemistry is not fully understood, in part because theoretical simulations of such systems are extremely expensive-with only smaller chromophores accessible. Here, we study the excited-state dynamics of tyrosine using a method based on deep neural networks that leverages the physics underlying quantum chemical data and combines different levels of theory. We reveal unconventional and dynamically controlled 'roaming' dynamics in excited tyrosine that are beyond chemical intuition and compete with other ultrafast deactivation mechanisms. Our findings suggest that the roaming atoms are radicals that can lead to photodamage, offering a new perspective on the photostability and photodamage of biological systems.

BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding Neural Network/Molecular Mechanics Simulations.

Hybrid quantum mechanics/molecular mechanics (QM/MM) simulations have advanced the field of computational chemistry tremendously. However, they require the partitioning of a system into two different regions that are treated at different levels of theory, which can cause artifacts at the interface. Furthermore, they are still limited by high computational costs of quantum chemical calculations. In this work, we develop the buffer region neural network (BuRNN), an alternative approach to existing QM/MM schemes, which introduces a buffer region that experiences full electronic polarization by the inner QM region to minimize artifacts. The interactions between the QM and the buffer region are described by deep neural networks (NNs), which leads to the high computational efficiency of this hybrid NN/MM scheme while retaining quantum chemical accuracy. We demonstrate the BuRNN approach by performing NN/MM simulations of the hexa-aqua iron complex.

Solving the electronic Schrödinger equation for multiple nuclear geometries with weight-sharing deep neural networks.

The Schrödinger equation describes the quantum-mechanical behaviour of particles, making it the most fundamental equation in chemistry. A solution for a given molecule allows computation of any of its properties. Finding accurate solutions for many different molecules and geometries is thus crucial to the discovery of new materials such as drugs or catalysts. Despite its importance, the Schrödinger equation is notoriously difficult to solve even for single molecules, as established methods scale exponentially with the number of particles. Combining Monte Carlo techniques with unsupervised optimization of neural networks was recently discovered as a promising approach to overcome this curse of dimensionality, but the corresponding methods do not exploit synergies that arise when considering multiple geometries. Here we show that sharing the vast majority of weights across neural network models for different geometries substantially accelerates optimization. Furthermore, weight-sharing yields pretrained models that require only a small number of additional optimization steps to obtain high-accuracy solutions for new geometries.

Activation by oxidation and ligand exchange in a molecular manganese vanadium oxide water oxidation catalyst.

Despite their technological importance for water splitting, the reaction mechanisms of most water oxidation catalysts (WOCs) are poorly understood. This paper combines theoretical and experimental methods to reveal mechanistic insights into the reactivity of the highly active molecular manganese vanadium oxide WOC [Mn4V4O17(OAc)3]3- in aqueous acetonitrile solutions. Using density functional theory together with electrochemistry and IR-spectroscopy, we propose a sequential three-step activation mechanism including a one-electron oxidation of the catalyst from [Mn2 3+Mn2 4+] to [Mn3+Mn3 4+], acetate-to-water ligand exchange, and a second one-electron oxidation from [Mn3+Mn3 4+] to [Mn4 4+]. Analysis of several plausible ligand exchange pathways shows that nucleophilic attack of water molecules along the Jahn-Teller axis of the Mn3+ centers leads to significantly lower activation barriers compared with attack at Mn4+ centers. Deprotonation of one water ligand by the leaving acetate group leads to the formation of the activated species [Mn4V4O17(OAc)2(H2O)(OH)]- featuring one H2O and one OH ligand. Redox potentials based on the computed intermediates are in excellent agreement with electrochemical measurements at various solvent compositions. This intricate interplay between redox chemistry and ligand exchange controls the formation of the catalytically active species. These results provide key reactivity information essential to further study bio-inspired molecular WOCs and solid-state manganese oxide catalysts.

Ultrafast photochemistry of a molybdenum carbonyl-nitrosyl complex with a triazacyclononane coligand.

Transition metal complexes capable of releasing small molecules such as carbon monoxide and nitric oxide upon photoactivation are versatile tools in various fields of chemistry and biology. In this work, we report on the ultrafast photochemistry of [Mo(CO)2(NO)(iPr3tacn)]PF6 (iPr3tacn = 1,4,7-triisopropyl-1,4,7-triazacyclononane), which was characterized under continuous illumination and with femtosecond UV-pump/UV-probe and UV-pump/MIR-probe spectroscopy, as well as with stationary calculations. The experimental and theoretical results demonstrate that while the photodissociation of one of the two CO ligands upon UV excitation can be inferred both on an ultrafast timescale as well as under exposure times of several minutes, no evidence of NO release is observed under the same conditions. The binding mode of the diatomic ligands is impacted by the electronic excitation, and transient intermediates are observed on a timescale of tens of picoseconds before CO is released from the coordination sphere. Furthermore, based on calculated potential energy scans, we suggest that photolysis of NO could be possible after a subsequent excitation of an electronically excited state with a second laser pulse, or by accessing low-lying excited states that otherwise cannot be directly excited by light.

Photo-Initiated Cobalt-Catalyzed Radical Olefin Hydrogenation.

Outer-sphere radical hydrogenation of olefins proceeds via stepwise hydrogen atom transfer (HAT) from transition metal hydride species to the substrate. Typical catalysts exhibit M-H bonds that are either too weak to efficiently activate H2 or too strong to reduce unactivated olefins. This contribution evaluates an alternative approach, that starts from a square-planar cobalt(II) hydride complex. Photoactivation results in Co-H bond homolysis. The three-coordinate cobalt(I) photoproduct binds H2 to give a dihydrogen complex, which is a strong hydrogen atom donor, enabling the stepwise hydrogenation of both styrenes and unactivated aliphatic olefins with H2 via HAT.

Tridentate 3-Substituted Naphthoquinone Ruthenium Arene Complexes: Synthesis, Characterization, Aqueous Behavior, and Theoretical and Biological Studies.

A series of nine RuII arene complexes bearing tridentate naphthoquinone-based N,O,O-ligands was synthesized and characterized. Aqueous stability and their hydrolysis mechanism were investigated via UV/vis photometry, HPLC-MS, and density functional theory calculations. Substituents with a positive inductive effect improved their stability at physiological pH (7.4) intensely, whereas substituents such as halogens accelerated hydrolysis and formation of dimeric pyrazolate and hydroxido bridged dimers. The observed cytotoxic profile is unusual, as complexes exhibited much higher cytotoxicity in SW480 colon cancer cells than in the broadly chemo- (incl. platinum-) sensitive CH1/PA-1 teratocarcinoma cells. This activity pattern as well as reduced or slightly enhanced ROS generation and the lack of DNA interactions indicate a mode of action different from established or previously investigated classes of metallodrugs.

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.

Excited-state dynamics of CH2I2 and CH2IBr studied with UV-pump VUV-probe momentum-resolved photoion spectroscopy.

We perform time-resolved ionization spectroscopy measurements of the excited state dynamics of CH2I2 and CH2IBr following photoexcitation in the deep UV. The fragment ions produced by ionization with a vacuum-ultraviolet probe pulse are measured with velocity map imaging, and the momentum resolved yields are compared with trajectory surface hopping calculations of the measurement observable. Together with recent time-resolved photoelectron spectroscopy measurements of the same dynamics, these results provide a detailed picture of the coupled electronic and nuclear dynamics involved. Our measurements highlight the non-adiabatic coupling between electronic states, which leads to notable differences in the dissociation dynamics for the two molecules.

Coherent Control of Internal Conversion in Strong-Field Molecular Ionization.

We demonstrate coherent control over internal conversion during strong-field molecular ionization with shaped, few-cycle laser pulses. The control is driven by interference in different neutral states, which are coupled via non-Born-Oppenheimer terms in the molecular Hamiltonian. Our measurements highlight the preservation of electronic coherence in nonadiabatic transitions between electronic states.

Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics.

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-multiple energies, forces, and different couplings-for photodynamics simulations. We simplify such simulations substantially by (i) a phase-free training skipping costly preprocessing of raw quantum chemistry data; (ii) rotationally covariant nonadiabatic couplings, which can either be trained or (iii) alternatively be approximated from only ML potentials, their gradients, and Hessians; and (iv) incorporating spin-orbit couplings. As the deep-learning method, we employ SchNet with its automatically determined representation of molecular structures and extend it for multiple electronic states. In combination with the molecular dynamics program SHARC, our approach termed SchNarc is tested on two polyatomic molecules and paves the way toward efficient photodynamics simulations of complex systems.

Machine learning enables long time scale molecular photodynamics simulations.

Photo-induced processes are fundamental in nature but accurate simulations of their dynamics 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.

OpenMolcas: From Source Code to Insight.

In this Article we describe the OpenMolcas environment and invite the computational chemistry community to collaborate. The open-source project already includes a large number of new developments realized during the transition from the commercial MOLCAS product to the open-source platform. The paper initially describes the technical details of the new software development platform. This is followed by brief presentations of many new methods, implementations, and features of the OpenMolcas program suite. These developments include novel wave function methods such as stochastic complete active space self-consistent field, density matrix renormalization group (DMRG) methods, and hybrid multiconfigurational wave function and density functional theory models. Some of these implementations include an array of additional options and functionalities. The paper proceeds and describes developments related to explorations of potential energy surfaces. Here we present methods for the optimization of conical intersections, the simulation of adiabatic and nonadiabatic molecular dynamics, and interfaces to tools for semiclassical and quantum mechanical nuclear dynamics. Furthermore, the Article describes features unique to simulations of spectroscopic and magnetic phenomena such as the exact semiclassical description of the interaction between light and matter, various X-ray processes, magnetic circular dichroism, and properties. Finally, the paper describes a number of built-in and add-on features to support the OpenMolcas platform with postcalculation analysis and visualization, a multiscale simulation option using frozen-density embedding theory, and new electronic and muonic basis sets.

Excited state dynamics of CH2I2 and CH2BrI studied with UV pump VUV probe photoelectron spectroscopy.

We compare the excited state dynamics of diiodomethane (CH2I2) and bromoiodomethane (CH2BrI) using time resolved photoelectron spectroscopy. A 4.65 eV UV pump pulse launches a dissociative wave packet on excited states of both molecules and the ensuing dynamics are probed via photoionization using a 7.75 eV probe pulse. The resulting photoelectrons are measured with the velocity map imaging technique for each pump-probe delay. Our measurements highlight differences in the dynamics for the two molecules, which are interpreted with high-level ab initio molecular dynamics (trajectory surface hopping) calculations. Our analysis allows us to associate features in the photoelectron spectrum with different portions of the excited state wave packet represented by different trajectories. The excited state dynamics in bromoiodomethane are simple and can be described in terms of direct dissociation along the C-I coordinate, whereas the dynamics in diiodomethane involve internal conversion and motion along multiple dimensions.