The non-adiabatic nanoreactor: towards the automated discovery of photochemistry

Boron-Nitrogen-Containing Benzene Valence Isomers

Benzene is one of the most ubiquitous structural motifs in chemistry. The valence isomers of benzene have also attracted synthetic chemists' attention due to their unique structures, bonding, and reactivity. We have been investigating boron-nitrogen-containing benzene valence isomers via photoisomerization of 1,2-azaborines. In this contribution, we summarize recent developments of these highly strained BN-heterocyclic compounds including their synthesis, characterization, proposed mechanism of formation, and their potential applications.

Reactant Discovery with an Ab Initio Nanoreactor: Exploration of Astrophysical N-Heterocycle Precursors and Formation Pathways

The incorporation of nitrogen atoms into cyclic compounds is essential for terrestrial life; nitrogen-containing (N-)heterocycles make up DNA and RNA nucleobases, several amino acids, B vitamins, porphyrins, and other components of biomolecules. The discovery of these molecules on meteorites with non-terrestrial isotopic abundances supports the hypothesis of exogenous delivery of prebiotic material to early Earth; however, there has been no detection of these species in interstellar environments, indicating that there is a need for greater knowledge of their astrochemical formation and destruction pathways. Here, we present results of simulations of gas-phase pyrrole and pyridine formation from an ab initio nanoreactor, a first-principles molecular dynamics simulation method that accelerates reaction discovery by applying non-equilibrium forces that are agnostic to individual reaction coordinates. Using the nanoreactor in a retrosynthetic mode, starting with the N-heterocycle of interest and a radical leaving group, then considering the discovered reaction pathways in reverse, a rich landscape of N-heterocycle-forming reactivity can be found. Several of these reaction pathways, when mapped to their corresponding minimum energy paths, correspond to novel barrierless formation pathways for pyridine and pyrrole, starting from both detected and hypothesized astrochemical precursors. This study demonstrates how first-principles reaction discovery can build mechanistic knowledge in astrochemical environments as well as in early Earth models such as Titan's atmosphere where N-heterocycles have been tentatively detected.

Photoisomerization pathways of trans-resveratrol

Resveratrol is well-known for promoting health benefits due to its antioxidant, anti-aging, anti-carcinogenic, and other beneficial activities. Understanding the photophysics of resveratrol is essential for determining its applicability to pharmaceutical innovations. In the present work, we used an explore-then-assess strategy to map the internal conversion pathways of trans-resveratrol. This strategy consists of exploring the multidimensional configurational space with nonadiabatic dynamics simulations based on a semiempirical multireference method, followed by a feasibility assessment of reduced-dimensionality pathways at a high ab initio theoretical level. The exploration step revealed that internal conversion to the ground state may occur near five distinct conical intersections. The assessment step showed that the main photoisomerization pathway involves a twisted-pyramidalized S1/S0 conical intersection, yielding either trans or cis isomers. However, a secondary path was identified, where cis-trans isomerization happens in the excited state and internal conversion occurs at a cyclic conical intersection, yielding a closed-ring resveratrol derivative. This derivative, which can be formed through this direct path or an indirect photoexcitation, may be connected to the production of oxygen-reactive species previously reported and have implications in photodynamic therapy.

A BN-Benzvalene

The synthesis and crystallographic characterization of BN-benzvalene, the first second-row heteroatom-containing benzvalene, is described. BN-benzvalenes are produced via photoexcitation of C5-aryl-substituted 1,2-azaborines under flow conditions. Mechanistic studies support a boron-specific, two-step photoisomerization pathway involving a BN-Dewar benzene intermediate, which is distinct from the photoisomerization pathway proposed in benzene and phospha- and silabenzenes for the formation of their respective benzvalene analogues.

Perspective on Theoretical and Experimental Advances in Atmospheric Photochemistry

Research that explores the chemistry of Earth's atmosphere is central to the current understanding of global challenges such as climate change, stratospheric ozone depletion, and poor air quality in urban areas. This research is a synergistic combination of three established domains: earth observation, for example, using satellites, and in situ field measurements; computer modeling of the atmosphere and its chemistry; and laboratory measurements of the properties and reactivity of gas-phase molecules and aerosol particles. The complexity of the interconnected chemical and photochemical reactions which determine the composition of the atmosphere challenges the capacity of laboratory studies to provide the spectroscopic, photochemical, and kinetic data required for computer models. Here, we consider whether predictions from computational chemistry using modern electronic structure theory and nonadiabatic dynamics simulations are becoming sufficiently accurate to supplement quantitative laboratory data for wavelength-dependent absorption cross-sections, photochemical quantum yields, and reaction rate coefficients. Drawing on presentations and discussions from the CECAM workshop on Theoretical and Experimental Advances in Atmospheric Photochemistry held in March 2024, we describe key concepts in the theory of photochemistry, survey the state-of-the-art in computational photochemistry methods, and compare their capabilities with modern experimental laboratory techniques. From such considerations, we offer a perspective on the scope of computational (photo)chemistry methods based on rigorous electronic structure theory to become a fourth core domain of research in atmospheric chemistry.

A Polarizable QM/MM Model That Combines the State-Averaged CASSCF and AMOEBA Force Field for Photoreactions in Proteins

This study presents the polarizable quantum mechanics/molecular mechanics (QM/MM) embedding of the state-averaged complete active space self-consistent field (SA-CASSCF) in the atomic multipole optimized energetics for biomolecular applications (AMOEBA) force field for the purpose of studying photoreactions in protein environments. We describe two extensions of our previous work that combine SA-CASSCF with AMOEBA water models, allowing it to be generalized to AMOEBA models for proteins and other macromolecules. First, we discuss how our QM/MM model accounts for the discrepancy between the direct and polarization electric fields that arises in the AMOEBA description of intramolecular polarization. A second improvement is the incorporation of link atom schemes to treat instances in which the QM/MM boundary goes through covalent bonds. A single-link atom scheme and double-link atom scheme are considered in this work, and we will discuss how electrostatic interaction, van der Waals interaction, and various kinds of valence terms are treated across the boundary. To test the accuracy of the link atom scheme, we will compare QM/MM with full QM calculations and study how the errors in ground state properties, excited state properties, and excitation energies change when tuning the parameters in the link atom scheme. We will also test the new SA-CASSCF/AMOEBA method on an elementary reaction step in NanoLuc, an artificial bioluminescence luciferase. We will show how the reaction mechanism is different when calculated in the gas phase, in polarizable continuum medium (PCM), versus in protein AMOEBA models.

Development of Parallel On-the-Fly Algorithm for Global Exploration of Conical Intersection Seam Space

Conical intersection (CI) seams are configuration spaces of a molecular system where two or more (spin) adiabatic electronic states are degenerate in energy. They play essential roles in photochemistry because nonradiative decays often occur near the minima of the seam, i.e., the minimum energy CIs (MECIs). Thus, it is important to explore the CI seams and discover the MECIs. Although various approaches exist for CI seam exploration, most of them are local in nature, requiring reasonable initial guesses of geometries and nuclear gradients during the search. Global search algorithms, on the other hand, are powerful because they can fully sample the configurational space and locate important MECIs missed by local algorithms. However, global algorithms are often computationally expensive for large systems due to their poor scalability with respect to the number of degrees of freedom. To overcome this challenge, we develop the parallel on-the-fly Crystal algorithm to globally explore the CI seam space, taking advantage of its superior scaling behavior. Specifically, Crystal is coupled with on-the-fly evaluations of the excited and ground state energies using multireference electronic structure methods. Meanwhile, the algorithm is parallelized to further boost its computational efficiency. The effectiveness of this new algorithm is tested for three types of molecular photoswitches of significant importance in material and biomedical sciences: photostatin (PST), stilbene, and butadiene. A rudimentary implementation of the algorithm is applied to PST and stilbene, resulting in the discovery of all previously identified MECIs and several new ones. A refined version of the algorithm, combined with a systematic clustering technique, is applied to butadiene, resulting in the identification of an unprecedented number of energetically accessible MECIs. The results demonstrate that the parallel on-the-fly Crystal algorithm is a powerful tool for automated global CI seam exploration.

CREST-A program for the exploration of low-energy molecular chemical space

Conformer-rotamer sampling tool (CREST) is an open-source program for the efficient and automated exploration of molecular chemical space. Originally developed in Pracht et al. [Phys. Chem. Chem. Phys. 22, 7169 (2020)] as an automated driver for calculations at the extended tight-binding level (xTB), it offers a variety of molecular- and metadynamics simulations, geometry optimization, and molecular structure analysis capabilities. Implemented algorithms include automated procedures for conformational sampling, explicit solvation studies, the calculation of absolute molecular entropy, and the identification of molecular protonation and deprotonation sites. Calculations are set up to run concurrently, providing efficient single-node parallelization. CREST is designed to require minimal user input and comes with an implementation of the GFNn-xTB Hamiltonians and the GFN-FF force-field. Furthermore, interfaces to any quantum chemistry and force-field software can easily be created. In this article, we present recent developments in the CREST code and show a selection of applications for the most important features of the program. An important novelty is the refactored calculation backend, which provides significant speed-up for sampling of small or medium-sized drug molecules and allows for more sophisticated setups, for example, quantum mechanics/molecular mechanics and minimum energy crossing point calculations.

Exploring Chemical Space Using Ab Initio Hyperreactor Dynamics

In recent years, first-principles exploration of chemical reaction space has provided valuable insights into intricate reaction networks. Here, we introduce ab initio hyperreactor dynamics, which enables rapid screening of the accessible chemical space from a given set of initial molecular species, predicting new synthetic routes that can potentially guide subsequent experimental studies. For this purpose, different hyperdynamics derived bias potentials are applied along with pressure-inducing spherical confinement of the molecular system in ab initio molecular dynamics simulations to efficiently enhance reactivity under mild conditions. To showcase the advantages and flexibility of the hyperreactor approach, we present a systematic study of the method's parameters on a HCN toy model and apply it to a recently introduced experimental model for the prebiotic formation of glycinal and acetamide in interstellar ices, which yields results in line with experimental findings. In addition, we show how the developed framework enables the study of complicated transitions like the first step of a nonenzymatic DNA nucleoside synthesis in an aqueous environment, where the molecular fragmentation problem of earlier nanoreactor approaches is avoided.

Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model

Transition state search is key in chemistry for elucidating reaction mechanisms and exploring reaction networks. The search for accurate 3D transition state structures, however, requires numerous computationally intensive quantum chemistry calculations due to the complexity of potential energy surfaces. Here we developed an object-aware SE(3) equivariant diffusion model that satisfies all physical symmetries and constraints for generating sets of structures-reactant, transition state and product-in an elementary reaction. Provided reactant and product, this model generates a transition state structure in seconds instead of hours, which is typically required when performing quantum-chemistry-based optimizations. The generated transition state structures achieve a median of 0.08 Å root mean square deviation compared to the true transition state. With a confidence scoring model for uncertainty quantification, we approach an accuracy required for reaction barrier estimation (2.6 kcal mol-1) by only performing quantum chemistry-based optimizations on 14% of the most challenging reactions. We envision usefulness for our approach in constructing large reaction networks with unknown mechanisms.

Efficient Acceleration of Reaction Discovery in the Ab Initio Nanoreactor: Phenyl Radical Oxidation Chemistry

Over the years, many computational strategies have been employed to elucidate reaction networks. One of these methods is accelerated molecular dynamics, which can circumvent the expense required in dynamics to find all reactants and products (local minima) and transition states (first-order saddle points) on a potential energy surface (PES) by using fictitious forces that promote reaction events. The ab initio nanoreactor uses these accelerating forces to study large chemical reaction networks from first-principles quantum mechanics. In the initial nanoreactor studies, this acceleration was done through a piston periodic compression potential, which pushes molecules together to induce entropically unfavorable bimolecular reactions. However, the piston is not effective for discovering intramolecular and dissociative reactions, such as those integral to the decomposition channels of phenyl radical oxidation. In fact, the choice of accelerating forces dictates not only the rate of reaction discovery but also the types of reactions discovered; thus, it is critical to understand the biases and efficacies of these forces. In this study, we examine forces using metadynamics, attractive potentials, and local thermostats for accelerating reaction discovery. For each force, we construct a separate phenyl radical combustion reaction network using solely that force in discovery trajectories. We elucidate the enthalpic and entropic trends of each accelerating force and highlight their efficiency in reaction discovery. Comparing the nanoreactor-constructed reaction networks with literature renditions of the phenyl radical combustion PES shows that a combination of accelerating forces is best suited for reaction discovery.

High-Throughput Screening for Ultrafast Photochemical Reaction Discovery

High-repetition-rate lasers present an opportunity to extend ultrafast spectroscopy from a detailed probe of singular model photochemical systems to a routine analysis technique in training machine learning models to aid the design cycle of photochemical syntheses. We bring together innovations in line scan cameras and micro-electro-mechanical grating modulators with sample delivery via high-pressure liquid chromatography pumps to demonstrate a transient absorption spectrometer that can characterize photoreactions initiated with ultrashort ultraviolet pulses in a time scale of minutes. Furthermore, we demonstrate that the ability to rapidly screen an important class of photochemical system, pyrimidine nucleosides, can be used to explore the effect of conformational modification on the evolution of excited-state processes.

An energy decomposition and extrapolation scheme for evaluating electron transfer rate constants: a case study on electron self-exchange reactions of transition metal complexes

A simple approach to the analysis of electron transfer (ET) reactions based on energy decomposition and extrapolation schemes is proposed. The present energy decomposition and extrapolation-based electron localization (EDEEL) method represents the diabatic energies for the initial and final states using the adiabatic energies of the donor and acceptor species and their complex. A scheme for the efficient estimation of ET rate constants is also proposed. EDEEL is semi-quantitative by directly evaluating the seam-of-crossing region of two diabatic potentials. In a numerical test, EDEEL successfully provided ET rate constants for electron self-exchange reactions of thirteen transition metal complexes with reasonable accuracy. In addition, its energy decomposition and extrapolation schemes provide all the energy values required for activation-strain model (ASM) analysis. The ASM analysis using EDEEL provided rational interpretations of the variation of the ET rate constants as a function of the transition metal complexes. These results suggest that EDEEL is useful for efficiently evaluating ET rate constants and obtaining a rational understanding of their magnitudes.

First principles reaction discovery: from the Schrodinger equation to experimental prediction for methane pyrolysis

Our recent success in exploiting graphical processing units (GPUs) to accelerate quantum chemistry computations led to the development of the ab initio nanoreactor, a computational framework for automatic reaction discovery and kinetic model construction. In this work, we apply the ab initio nanoreactor to methane pyrolysis, from automatic reaction discovery to path refinement and kinetic modeling. Elementary reactions occurring during methane pyrolysis are revealed using GPU-accelerated ab initio molecular dynamics simulations. Subsequently, these reaction paths are refined at a higher level of theory with optimized reactant, product, and transition state geometries. Reaction rate coefficients are calculated by transition state theory based on the optimized reaction paths. The discovered reactions lead to a kinetic model with 53 species and 134 reactions, which is validated against experimental data and simulations using literature kinetic models. We highlight the advantage of leveraging local brute force and Monte Carlo sensitivity analysis approaches for efficient identification of important reactions. Both sensitivity approaches can further improve the accuracy of the methane pyrolysis kinetic model. The results in this work demonstrate the power of the ab initio nanoreactor framework for computationally affordable systematic reaction discovery and accurate kinetic modeling.

Finding Excited-State Minimum Energy Crossing Points on a Budget: Non-Self-Consistent Tight-Binding Methods

The automated exploration and identification of minimum energy conical intersections (MECIs) is a valuable computational strategy for the study of photochemical processes. Due to the immense computational effort involved in calculating non-adiabatic derivative coupling vectors, simplifications have been introduced focusing instead on minimum energy crossing points (MECPs), where promising attempts were made with semiempirical quantum mechanical methods. A simplified treatment for describing crossing points between almost arbitrary diabatic states based on a non-self-consistent extended tight-binding method, GFN0-xTB, is presented. Involving only a single diagonalization of the Hamiltonian, the method can provide energies and gradients for multiple electronic states, which can be used in a derivative coupling-vector-free scheme to calculate MECPs. By comparison with high-lying MECIs of benchmark systems, it is demonstrated that the identified geometries are good starting points for further MECI refinement with ab initio methods.

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.

Prediction of transition state structures of gas-phase chemical reactions via machine learning

The elucidation of transition state (TS) structures is essential for understanding the mechanisms of chemical reactions and exploring reaction networks. Despite significant advances in computational approaches, TS searching remains a challenging problem owing to the difficulty of constructing an initial structure and heavy computational costs. In this paper, a machine learning (ML) model for predicting the TS structures of general organic reactions is proposed. The proposed model derives the interatomic distances of a TS structure from atomic pair features reflecting reactant, product, and linearly interpolated structures. The model exhibits excellent accuracy, particularly for atomic pairs in which bond formation or breakage occurs. The predicted TS structures yield a high success ratio (93.8%) for quantum chemical saddle point optimizations, and 88.8% of the optimization results have energy errors of less than 0.1 kcal mol-1. Additionally, as a proof of concept, the exploration of multiple reaction paths of an organic reaction is demonstrated based on ML inferences. I envision that the proposed approach will aid in the construction of initial geometries for TS optimization and reaction path exploration.

TeraChem protocol buffers (TCPB): Accelerating QM and QM/MM simulations with a client-server model

The routine use of electronic structures in many chemical simulation applications calls for efficient and easy ways to access electronic structure programs. We describe how the graphics processing unit (GPU) accelerated electronic structure program TeraChem can be set up as an electronic structure server, to be easily accessed by third-party client programs. We exploit Google's protocol buffer framework for data serialization and communication. The client interface, called TeraChem protocol buffers (TCPB), has been designed for ease of use and compatibility with multiple programming languages, such as C++, Fortran, and Python. To demonstrate the ease of coupling third-party programs with electronic structures using TCPB, we have incorporated the TCPB client into Amber for quantum mechanics/molecular mechanics (QM/MM) simulations. The TCPB interface saves time with GPU initialization and I/O operations, achieving a speedup of more than 2× compared to a prior file-based implementation for a QM region with ∼250 basis functions. We demonstrate the practical application of TCPB by computing the free energy profile of p-hydroxybenzylidene-2,3-dimethylimidazolinone (p-HBDI^-)-a model chromophore in green fluorescent proteins-on the first excited singlet state using Hamiltonian replica exchange for enhanced sampling. All calculations in this work have been performed with the non-commercial freely-available version of TeraChem, which is sufficient for many QM region sizes in common use.

Automated Reaction Kinetics of Gas-Phase Organic Species over Multiwell Potential Energy Surfaces

Automation of rate-coefficient calculations for gas-phase organic species became possible in recent years and has transformed how we explore these complicated systems computationally. Kinetics workflow tools bring rigor and speed and eliminate a large fraction of manual labor and related error sources. In this paper we give an overview of this quickly evolving field and illustrate, through five detailed examples, the capabilities of our own automated tool, KinBot. We bring examples from combustion and atmospheric chemistry of C-, H-, O-, and N-atom-containing species that are relevant to molecular weight growth and autoxidation processes. The examples shed light on the capabilities of automation and also highlight particular challenges associated with the various chemical systems that need to be addressed in future work.

Automated QM/MM Screening of Rhodopsin Variants with Enhanced Fluorescence

We present a computational protocol for the fast and automated screening of excited-state hybrid quantum mechanics/molecular mechanics (QM/MM) models of rhodopsins to be used as fluorescent probes based on the automatic rhodopsin modeling protocol (a-ARM). Such "a-ARM fluorescence screening protocol" is implemented through a general Python-based driver, PyARM, that is also proposed here. The implementation and performance of the protocol are benchmarked using different sets of rhodopsin variants whose absorption and, more relevantly, emission spectra have been experimentally measured. We show that, despite important limitations that make unsafe to use it as a black-box tool, the protocol reproduces the observed trends in fluorescence and it is capable of selecting novel potentially fluorescent rhodopsins. We also show that the protocol can be used in mechanistic investigations to discern fluorescence enhancement effects associated with a near degeneracy of the S₁/S₂ states or, alternatively, with a barrier generated via coupling of the S₀/S₁ wave functions.

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.

Multi-state Energy Landscape for Photoreaction of Stilbene and Dimethyl-stilbene

We have recently developed the reaction space projector (ReSPer) method, which constructs a reduced-dimensionality reaction space uniquely determined from reference reaction paths for a polyatomic molecular system and projects classical trajectories into the same reaction space. In this paper, we extend ReSPer to the analysis of photoreaction dynamics and relaxation processes of stilbene and present the concept of a "multi-state energy landscape," incorporating the ground- and excited-state reaction subspaces. The multi-state energy landscape successfully explains the previously established photoreaction processes of cis-stilbene, such as the cis-trans photoisomerization and photocyclization. In addition, we discuss the difference in the excited-state reaction dynamics between stilbene and 1,1'-dimethyl stilbene based on a common reaction subspace determined from the framework part of reference structures with different number of atoms. This approach allows us to target any molecule with a common framework, greatly expanding the applicability of the ReSPer analysis. The multi-state energy landscape provides fruitful insight into photochemical reactions, exploring the excited- and ground-state potential energy surfaces, as well as comprehensive reaction processes with nonradiative transitions between adiabatic states, within the stage of a reduced-dimensionality reaction space.

Machine learning the Hohenberg-Kohn map for molecular excited states

The Hohenberg-Kohn theorem of density-functional theory establishes the existence of a bijection between the ground-state electron density and the external potential of a many-body system. This guarantees a one-to-one map from the electron density to all observables of interest including electronic excited-state energies. Time-Dependent Density-Functional Theory (TDDFT) provides one framework to resolve this map; however, the approximations inherent in practical TDDFT calculations, together with their computational expense, motivate finding a cheaper, more direct map for electronic excitations. Here, we show that determining density and energy functionals via machine learning allows the equations of TDDFT to be bypassed. The framework we introduce is used to perform the first excited-state molecular dynamics simulations with a machine-learned functional on malonaldehyde and correctly capture the kinetics of its excited-state intramolecular proton transfer, allowing insight into how mechanical constraints can be used to control the proton transfer reaction in this molecule. This development opens the door to using machine-learned functionals for highly efficient excited-state dynamics simulations.

Fully Automated Generation of Prebiotically Relevant Reaction Networks from Optimized Nanoreactor Simulations

The nanoreactor approach first introduced by the group of Martı́nez [Wang et al. Nat. Chem. 2014, 6, 1044-1048] has recently attracted much attention because of its ability to accelerate the discovery of reaction pathways. Here, we provide a comprehensive study of various simulation parameters and present an alternative implementation for the reactivity-enhancing spherical constraint function, as well as for the detection of reaction events. In this context, a fully automated postsimulation evaluation procedure based on RDKit and NetworkX analysis is introduced. The chemical and physical robustness of the procedure is examined by investigating the reactivity of selected homogeneous systems. The optimized procedure is applied at the GFN2-xTB level of theory to a system composed of HCN molecules and argon atoms, acting as a buffer, yielding prebiotically plausible primary and secondary precursors for the synthesis of RNA. Furthermore, the formose reaction network is explored leading to numerous sugar precursors. The discovered compounds reflect experimental findings; however, new synthetic routes and a large collection of exotic, highly reactive molecules are observed, highlighting the predictive power of the nanoreactor approach for unraveling the reactive manifold.

Graph-Driven Reaction Discovery: Progress, Challenges, and Future Opportunities

Graph-based descriptors, such as bond-order matrices and adjacency matrices, offer a simple and compact way of categorizing molecular structures; furthermore, such descriptors can be readily used to catalog chemical reactions (i.e., bond-making and -breaking). As such, a number of graph-based methodologies have been developed with the goal of automating the process of generating chemical reaction network models describing the possible mechanistic chemistry in a given set of reactant species. Here, we outline the evolution of these graph-based reaction discovery schemes, with particular emphasis on more recent methods incorporating graph-based methods with semiempirical and ab initio electronic structure calculations, minimum-energy path refinements, and transition state searches. Using representative examples from homogeneous catalysis and interstellar chemistry, we highlight how these schemes increasingly act as "virtual reaction vessels" for interrogating mechanistic questions. Finally, we highlight where challenges remain, including issues of chemical accuracy and calculation speeds, as well as the inherent challenge of dealing with the vast size of accessible chemical reaction space.

Enhanced Sampling Aided Design of Molecular Photoswitches

Advances in the evolving field of atomistic simulations promise important insights for the design and fundamental understanding of novel molecular photoswitches. Here, we use state-of-the-art enhanced simulation techniques to unravel the complex, multistep chemistry of donor-acceptor Stenhouse adducts (DASAs). Our reaction discovery workflow consists of enhanced sampling for efficient chemical space exploration, refinement of newly observed pathways with more accurate ab initio electronic structure calculations, and structural modifications to introduce design principles within future generations of DASAs. We showcase our discovery workflow by not only recovering the full photoswitching mechanism of DASA but also predicting a plethora of new plausible thermal pathways and suggesting a way for their experimental validation. Furthermore, we illustrate the tunability of these newly discovered reactions, leading to a potential avenue for controlling DASA dynamics through multiple external stimuli. Overall, these insights could offer alternative routes to increase the efficiency and control of DASA's photoswitching mechanism, providing new elements to design more complex light-responsive materials.

Fast Screening of Minimum Energy Crossing Points with Semiempirical Tight-Binding Methods

The investigation of photochemical processes is a highly active field in computational chemistry. One research direction is the automated exploration and identification of minimum energy conical intersection (MECI) geometries. However, due to the immense technical effort required to calculate nonadiabatic potential energy landscapes, the routine application of such computational protocols is severely limited. In this study, we will discuss the prospect of combining adiabatic potential energy surfaces from semiempirical quantum mechanical calculations with specialized confinement potential and metadynamics simulations to identify S₀/T₁ minimum energy crossing point (MECP) geometries. It is shown that MECPs calculated at the GFN2-xTB level can provide suitable approximations to high-level S₀/S₁ab initio conical intersection geometries at a fraction of the computational cost. Reference MECIs of benzene are studied to illustrate the basic concept. An example application of the presented protocol is demonstrated for a set of photoswitch molecules.

Excited-State Distortions Promote the Photochemical 4π-Electrocyclizations of Fluorobenzenes via Machine Learning Accelerated Photodynamics Simulations

Benzene fluorination increases chemoselectivities for Dewar-benzenes via 4π-disrotatory electrocyclization. However, the origin of the chemo- and regioselectivities of fluorobenzenes remains unexplained because of the experimental limitations in resolving the excited-state structures on ultrafast timescales. The computational cost of multiconfigurational nonadiabatic molecular dynamics simulations is also currently cost-prohibitive. We now provide high-fidelity structural information and reaction outcome predictions with machine-learning-accelerated photodynamics simulations of a series of fluorobenzenes, C₆ F_6-n H_n , n=0-3, to study their S₁ →S₀ decay in 4 ns. We trained neural networks with XMS-CASPT2(6,7)/aug-cc-pVDZ calculations, which reproduced the S₁ absorption features with mean absolute errors of 0.04 eV (<2 nm). The predicted nonradiative decay constants for C₆ F₄ H₂ , C₆ F₆ , C₆ F₃ H₃ , and C₆ F₅ H are 116, 60, 28, and 12 ps, respectively, in broad qualitative agreement with the experiments. Our calculations show that a pseudo Jahn-Teller distortion of fluorinated benzenes leads to an S₁ local-minimum region that extends the excited-state lifetimes of fluorobenzenes. The pseudo Jahn-Teller distortions reduce when fluorination decreases. Our analysis of the S₁ dynamics shows that the pseudo-Jahn-Teller distortions promote an excited-state cis-trans isomerization of a π_C-C bond. We characterized the surface hopping points from our NAMD simulations and identified instantaneous nuclear momentum as a factor that promotes the electrocyclizations.

Photochemically Induced 1,3-Butadiene Ring-Closure from the Topological Analysis of the Electron Localization Function Viewpoint

The electronic rearrangement featuring the photochemically-induced 1,3-cis-butadiene is discussed within a bonding evolution theory (BET) perspective based on the topological analysis of the electron localization function and Thom's catastrophe theory. The process involves the vertical singlet-singlet excitation S₀ →S₂ , and the subsequent deactivation implying the S₂ /S₁ and S₁ /S₀ conical intersection regions. BET results reveal that the new CC bond is finally formed on the S₀ surface, as also recently found in the photochemical addition of two ethylenes [Phys. Chem. Chem. Phys. 23, 20598, (2021)].

Discover, Sample, and Refine: Exploring Chemistry with Enhanced Sampling Techniques

Over the last few decades, enhanced sampling methods have been continuously improved. Here, we exploit this progress and propose a modular workflow for blind reaction discovery and determination of reaction paths. In a three-step strategy, at first we use a collective variable derived from spectral graph theory in conjunction with the explore variant of the on-the-fly probability enhanced sampling method to drive reaction discovery runs. Once different chemical products are determined, we construct an ad-hoc neural network-based collective variable to improve sampling, and finally we refine the results using the free energy perturbation theory and a more accurate Hamiltonian. We apply this strategy to both intramolecular and intermolecular reactions. Our workflow requires minimal user input and extends the power of ab initio molecular dynamics to explore and characterize the reaction space.

New Tools for Taming Complex Reaction Networks: The Unimolecular Decomposition of Indole Revisited

The level of detail attained in the computational description of reaction mechanisms can be vastly improved through tools for automated chemical space exploration, particularly for systems of small to medium size. Under this approach, the unimolecular decomposition landscape for indole was explored through the automated reaction mechanism discovery program AutoMeKin. Nevertheless, the sheer complexity of the obtained mechanisms might be a hindrance regarding their chemical interpretation. In this spirit, the new Python library amk-tools has been designed to read and manipulate complex reaction networks, greatly simplifying their overall analysis. The package provides interactive dashboards featuring visualizations of the network, the three-dimensional (3D) molecular structures and vibrational normal modes of all chemical species, and the corresponding energy profiles for selected pathways. The combination of the joined mechanism generation and postprocessing workflow with the rich chemistry of indole decomposition enabled us to find new details of the reaction (obtained at the CCSD(T)/aug-cc-pVTZ//M06-2X/MG3S level of theory) that were not reported before: (i) 16 pathways leading to the formation of HCN and NH₃ (via amino radical); (ii) a barrierless reaction between methylene radical and phenyl isocyanide, which might be an operative mechanism under the conditions of the interstellar medium; and (iii) reaction channels leading to both hydrogen cyanide and hydrogen isocyanide, of potential astrochemical interest as the computed HNC/HCN ratios greatly exceed the calculated equilibrium value at very low temperatures. The reported reaction networks can be very valuable to supplement databases of kinetic data, which is of remarkable interest for pyrolysis and astrochemical studies.

Active orbital preservation for multiconfigurational self-consistent field

We introduce Active Orbital Preservation for Multiconfigurational Self-Consistent Field (AOP-MCSCF), an automated approach to improving the consistency of active space orbitals over multiple molecular configurations. Our approach is based on maximum overlap with a reference set of active space orbitals taken from a single geometry of a chromophore in the gas phase and can be used to automatically preserve the appropriate orbitals of the chromophore across multiple thermally sampled configurations, even when the chromophore is solvated by quantum-mechanically treated water molecules. In particular, using the singular value decomposition of a Molecular Orbital (MO) overlap matrix between the system and reference, we rotate the MOs of the system to align with the reference active space orbitals and use the resulting rotated orbitals as an initial guess to a MCSCF calculation. We demonstrate the approach on aqueous p-hydroxybenzylidene-imidazolinone (HBI) and find that AOP-MCSCF converges to the "correct" orbitals for over 90% of 3000 thermally sampled configurations. In addition, we compute the linear absorption spectrum and find excellent agreement with new experimental measurements up to 5.4 eV (230 nm). We show that electrostatic contributions to the solvation energy of HBI largely explain the observed state-dependent solvatochromism.