Reparameterization of GFN1-xTB for atmospheric molecular clusters: applications to multi-acid-multi-base systems.

Atmospheric molecular clusters, the onset of secondary aerosol formation, are a major part of the current uncertainty in modern climate models. Quantum chemical (QC) methods are usually employed in a funneling approach to identify the lowest free energy cluster structures. However, the funneling approach highly depends on the accuracy of low-cost methods to ensure that important low-lying minima are not missed. Here we present a reparameterized GFN1-xTB model based on the clusteromics I-V datasets for studying atmospheric molecular clusters (AMC), denoted AMC-xTB. The AMC-xTB model reduces the mean of electronic binding energy errors from 7-11.8 kcal mol-1 to roughly 0 kcal mol-1 and the root mean square deviation from 7.6-12.3 kcal mol-1 to 0.81-1.45 kcal mol-1. In addition, the minimum structures obtained with AMC-xTB are closer to the ωB97X-D/6-31++G(d,p) level of theory compared to GFN1-xTB. We employ the new parameterization in two new configurational sampling workflows that include an additional meta-dynamics sampling step using CREST with the AMC-xTB model. The first workflow, denoted the "independent workflow", is a commonly used funneling approach with an additional CREST step, and the second, the "improvement workflow", is where the best configuration currently known in the literature is improved with a CREST + AMC-xTB step. Testing the new workflow we find configurations lower in free energy for all the literature clusters with the largest improvement being up to 21 kcal mol-1. Lastly, by employing the improvement workflow we massively screened 288 new multi-acid-multi-base clusters containing up to 8 different species. For these new multi-acid-multi-base cluster systems we observe that the improvement workflow finds configurations lower in free energy for 245 out of 288 (85.1%) cluster structures. Most of the improvements are within 2 kcal mol-1, but we see improvements up to 8.3 kcal mol-1. Hence, we can recommend this new workflow based on the AMC-xTB model for future studies on atmospheric molecular clusters.

Improved Configurational Sampling Protocol for Large Atmospheric Molecular Clusters.

The nucleation process leading to the formation of new atmospheric particles plays a crucial role in aerosol research. Quantum chemical (QC) calculations can be used to model the early stages of aerosol formation, where atmospheric vapor molecules interact and form stable molecular clusters. However, QC calculations heavily depend on the chosen computational method, and when dealing with large systems, striking a balance between accuracy and computational cost becomes essential. We benchmarked the binding energies and structures and found the B97-3c method to be a good compromise between the accuracy and computational cost for studying large cluster systems. Further, we carefully assessed configurational sampling procedures for targeting large atmospheric molecular clusters containing up to 30 molecules (approximately 2 nm in diameter) and proposed a funneling approach with highly improved accuracy. We find that several parallel ABCluster explorations lead to better guesses for the cluster global energy minimum structures than one long exploration. This methodology allows us to bridge computational studies of molecular clusters, which typically reach only around 1 nm, with experimental studies that often measure particles larger than 2 nm. By employing this workflow, we searched for low-energy configurations of large sulfuric acid-ammonia and sulfuric acid-dimethylamine clusters. We find that the binding free energies of clusters containing dimethylamine are unequivocally more stable than those of the ammonia-containing clusters. Our improved configurational sampling protocol can in the future be applied to study the growth and dynamics of large clusters of arbitrary compositions.

Computational Tools for Handling Molecular Clusters: Configurational Sampling, Storage, Analysis, and Machine Learning.

Computational modeling of atmospheric molecular clusters requires a comprehensive understanding of their complex configurational spaces, interaction patterns, stabilities against fragmentation, and even dynamic behaviors. To address these needs, we introduce the Jammy Key framework, a collection of automated scripts that facilitate and streamline molecular cluster modeling workflows. Jammy Key handles file manipulations between varieties of integrated third-party programs. The framework is divided into three main functionalities: (1) Jammy Key for configurational sampling (JKCS) to perform systematic configurational sampling of molecular clusters, (2) Jammy Key for quantum chemistry (JKQC) to analyze commonly used quantum chemistry output files and facilitate database construction, handling, and analysis, and (3) Jammy Key for machine learning (JKML) to manage machine learning methods in optimizing molecular cluster modeling. This automation and machine learning utilization significantly reduces manual labor, greatly speeds up the search for molecular cluster configurations, and thus increases the number of systems that can be studied. Following the example of the Atmospheric Cluster Database (ACDB) of Elm (ACS Omega, 4, 10965-10984, 2019), the molecular clusters modeled in our group using the Jammy Key framework have been stored in an improved online GitHub repository named ACDB 2.0. In this work, we present the Jammy Key package alongside its assorted applications, which underline its versatility. Using several illustrative examples, we discuss how to choose appropriate combinations of methodologies for treating particular cluster types, including reactive, multicomponent, charged, or radical clusters, as well as clusters containing flexible or multiconformer monomers or heavy atoms. Finally, we present a detailed example of using the tools for atmospheric acid-base clusters.

Toward Modeling the Growth of Large Atmospheric Sulfuric Acid-Ammonia Clusters.

Studying large atmospheric molecular clusters is needed to understand the transition between clusters and aerosol particles. In this work, we studied the (SA)n(AM)n clusters with n up to 30 and the (SA)m(AM)m±2 clusters, with m = 6-20. The cluster configurations are sampled using the ABCluster program, and the cluster geometries and thermochemical parameters are calculated using GFN1-xTB. The cluster binding energies are calculated using B97-3c. We find that the addition of sulfuric acid is preferred to the addition of ammonia. The addition free energies were found to have large uncertainties, which could potentially be attributed to errors in the applied level of theory. Based on DLPNO-CCSD(T0)/aug-cc-pVTZ benchmarks of the binding energies of the large (SA)8-9(AM)10 and (SA)10(AM)10-11 clusters, we find that ωB97X-D3BJ with a large basis set is required to yield accurate binding and addition energies. However, based on recalculations of the single-point energy at r2SCAN-3c and ωB97X-D3BJ/6-311++G(3df,3pd), we show that the single-point energy contribution is not the primary source of error. We hypothesize that a larger source of error might be present in the form of insufficient configurational sampling. Finally, we train Δ machine learning model on (SA)n(AM)n clusters with n up to 5 and show that we can predict the binding energies of clusters up to sizes of (SA)30(AM)30 with a binding energy error below 0.6 %. This is an encouraging approach for accurately modeling the binding energies of large acid-base clusters in the future.

Nitric Acid and Organic Acids Suppress the Role of Methanesulfonic Acid in Atmospheric New Particle Formation.

Multicomponent atmospheric molecular clusters, typically comprising a combination of acids and bases, play a pivotal role in our climate system and contribute to the perplexing uncertainties embedded in modern climate models. Our understanding of cluster formation is limited by the lack of studies on complex mixed-acid-mixed-base systems. Here, we investigate multicomponent clusters consisting of mixtures of several acid and base molecules: sulfuric acid (SA), methanesulfonic acid (MSA), nitric acid (NA), formic acid (FA), along with methylamine (MA), dimethylamine (DMA), and trimethylamine (TMA). We calculated the binding free energies of a comprehensive set of 252 mixed-acid-mixed-base clusters at the DLPNO-CCSD(T0)/aug-cc-pVTZ//ωB97X-D/6-31++G(d,p) level of theory. Combined with the existing datasets, we simulated the new particle formation (NPF) rates using the Atmospheric Cluster Dynamics Code (ACDC). We find that the presence of NA and FA had a substantial impact, increasing the NPF rate by 60% at realistic conditions. Intriguingly, we find that NA and FA suppress the role of MSA in NPF. These findings suggest that even high concentration of MSA has a limited impact on NPF in polluted regions with high FA and NA. We outline a method for generating a lookup table that could potentially be used in climate models that sufficiently incorporates all the required chemistry. By unraveling the molecular mechanisms of mixed-acid-mixed-base clusters, we get one step closer to comprehending their implications for our global climate system.

Clusterome: A Comprehensive Data Set of Atmospheric Molecular Clusters for Machine Learning Applications.

Formation and growth of atmospheric molecular clusters into aerosol particles impact the global climate and contribute to the high uncertainty in modern climate models. Cluster formation is usually studied using quantum chemical methods, which quickly becomes computationally expensive when system sizes grow. In this work, we present a large database of ∼250k atmospheric relevant cluster structures, which can be applied for developing machine learning (ML) models. The database is used to train the ML model kernel ridge regression (KRR) with the FCHL19 representation. We test the ability of the model to extrapolate from smaller clusters to larger clusters, between different molecules, between equilibrium structures and out-of-equilibrium structures, and the transferability onto systems with new interactions. We show that KRR models can extrapolate to larger sizes and transfer acid and base interactions with mean absolute errors below 1 kcal/mol. We suggest introducing an iterative ML step in configurational sampling processes, which can reduce the computational expense. Such an approach would allow us to study significantly more cluster systems at higher accuracy than previously possible and thereby allow us to cover a much larger part of relevant atmospheric compounds.

Limited Role of Malonic Acid in Sulfuric Acid-Dimethylamine New Particle Formation.

Aerosols play an important role in climate and air quality; however, the mechanisms behind aerosol particle formation in the atmosphere are poorly understood. Studies have identified sulfuric acid, water, oxidized organics, and ammonia/amines as key precursors for forming aerosol particles in the atmosphere. Theoretical and experimental investigations have indicated that other species, such as organic acids, may be involved in atmospheric nucleation and growth of freshly formed aerosol particles. Organic acids, such as dicarboxylic acids, which are abundant in the atmosphere, have been measured in ultrafine aerosol particles. These observations suggest that organic acids may contribute to new particle formation in the atmosphere but their role remains ambiguous. This study examines how malonic acid interacts with sulfuric acid and dimethylamine to form new particles at warm boundary layer conditions using experimental observations from a laminar flow reactor and quantum chemical calculations coupled with cluster dynamics simulations. Observations reveal that malonic acid does not contribute to the initial steps (formation of <1 nm diameter particle) of nucleation with sulfuric acid-dimethylamine. In addition, malonic acid was found to not participate in the subsequent growth of the freshly nucleated 1 nm particles from sulfuric acid-dimethylamine reactions to diameters of 2 nm.

Heterogeneous Ion-Induced Nucleation of Water and Butanol Vapors Studied via Computational Quantum Chemistry beyond Prenucleation and Critical Cluster Sizes.

Water and butanol are used as working fluids in condensation particle counters, and condensation of a single vapor onto an ion can be used as a simple model system for the study of ion-induced nucleation in the atmosphere. Motivated by this, we examine heterogeneous nucleation of water (H2O) and n-butanol (BuOH) vapors onto three positively (Li+, Na+, K+) and three negatively charged (F-, Cl-, Br-) ions using classical nucleation theory and computational quantum chemistry methods. We study phenomena that cannot be captured by Kelvin-Thomson equation for small nucleation ion cores. Our quantum chemistry calculations reveal the molecular mechanism behind ion-induced nucleation for each studied system. Typically, ions become solvated from all sides after several vapor molecules condense onto the ion. However, we show that the clusters of water and large negatively charged ions (Cl- and Br-) thermodynamically prefer the ion being migrated to the cluster surface. Although our methods generally do not show clear sign-preference for ion-water nucleation, we identified positive sign-preference for ion-butanol nucleation caused by the possibility to form stabilizing hydrogen bonds between butanol molecules condensed onto a positively charged ion. These bonds cannot form when butanol condenses onto a negatively charged ion. Therefore, we show that ion charge, its sign, as well as vapor properties have effects on the prenucleation and critical cluster/droplet sizes and also on the molecular mechanism of ion-induced nucleation.

Atmospheric Sulfuric Acid-Multi-Base New Particle Formation Revealed through Quantum Chemistry Enhanced by Machine Learning.

The formation of molecular clusters and secondary aerosols in the atmosphere has a significant impact on the climate. Studies typically focus on the new particle formation (NPF) of sulfuric acid (SA) with a single base molecule (e.g., dimethylamine or ammonia). In this work, we examine the combinations and synergy of several bases. Specifically, we used computational quantum chemistry to perform configurational sampling (CS) of (SA)0-4(base)0-4 clusters with five different types of bases: ammonia (AM), methylamine (MA), dimethylamine (DMA), trimethylamine (TMA), and ethylenediamine (EDA). Overall, we studied 316 different clusters. We used a traditional multilevel funnelling sampling approach augmented by a machine-learning (ML) step. The ML made the CS of these clusters possible by significantly enhancing the speed and quality of the search for the lowest free energy configurations. Subsequently, the cluster thermodynamics properties were evaluated at the DLPNO-CCSD(T0)/aug-cc-pVTZ//ωB97X-D/6-31++G(d,p) level of theory. The calculated binding free energies were used to evaluate the cluster stabilities for population dynamics simulations. The resultant SA-driven NPF rates and synergies of the studied bases are presented to show that DMA and EDA act as nucleators (although EDA becomes weak in large clusters), TMA acts as a catalyzer, and AM/MA is often overshadowed by strong bases.

Current and future machine learning approaches for modeling atmospheric cluster formation.

The formation of strongly bound atmospheric molecular clusters is the first step towards forming new aerosol particles. Recent advances in the application of machine learning models open an enormous opportunity for complementing expensive quantum chemical calculations with efficient machine learning predictions. In this Perspective, we present how data-driven approaches can be applied to accelerate cluster configurational sampling, thereby greatly increasing the number of chemically relevant systems that can be covered.

Massive Assessment of the Binding Energies of Atmospheric Molecular Clusters.

Quantum chemical studies of the formation and growth of atmospheric molecular clusters are important for understanding aerosol particle formation. However, the search for the lowest free-energy cluster configuration is extremely time consuming. This makes high-level benchmark data sets extremely valuable in the quest for the global minimum as it allows the identification of cost-efficient computational methodologies, as well as the development of high-level machine learning (ML) models. Herein, we present a highly versatile quantum chemical data set comprising a total of 11 749 (acid)1-2(base)1-2 cluster configurations, containing up to 44 atoms. Utilizing the LUMI supercomputer, we calculated highly accurate PNO-CCSD(F12*)(T)/cc-pVDZ-F12 binding energies of the full set of cluster configurations leading to an unprecedented data set both in regard to sheer size and with respect to the level of theory. We employ the constructed benchmark set to assess the performance of various semiempirical and density functional theory methods. In particular, we find that the r2-SCAN-3c method shows excellent performance across the data set related to both accuracy and CPU time, making it a promising method to employ during cluster configurational sampling. Furthermore, applying the data sets, we construct ML models based on Δ-learning and provide recommendations for future application of ML in cluster configurational sampling.

Contribution of Methanesulfonic Acid to the Formation of Molecular Clusters in the Marine Atmosphere.

Because of the lack of long-term measurements, new particle formation (NPF) in the marine atmosphere remains puzzling. Using quantum chemical methods, this study elucidates the cluster formation and further growth of sulfuric acid-methanesulfonic acid-dimethylamine (SA-MSA-DMA) clusters, relevant to NPF in the marine atmosphere. The cluster structures and thermochemical parameters of (SA)n(MSA)m(DMA)l (n + m ≤ 4 and l ≤ 4) systems are calculated using density functional theory at the ωB97X-D/6-31++G(d,p) level of theory, and the single-point energies are calculated using high-level DLPNO-CCSD(T0)/aug-cc-pVTZ calculations. The calculated thermochemistry is used as input to the Atmospheric Cluster Dynamics Code (ACDC) to gain insight into the cluster dynamics. At ambient conditions (298.15 K, 1 atm), we find that the distribution of outgrowing clusters primarily consists of SA and DMA, with a minor contribution from the mixed SA-MSA-DMA clusters. At lower temperature (278.15 K, 1 atm) the distribution broadens, and clusters containing one or more MSA molecules emerge. These findings show that in the cold marine atmosphere MSA likely participates in atmospheric NPF.

New Particle Formation from the Vapor Phase: From Barrier-Controlled Nucleation to the Collisional Limit.

Studies of vapor phase nucleation have largely been restricted to one of two limiting cases-nucleation controlled by a substantial free energy barrier or the collisional limit where the barrier is negligible. For weakly bound systems, exploring the transition between these regimes has been an experimental challenge, and how nucleation evolves in this transition remains an open question. We overcome these limitations by combining complementary Laval expansion experiments, providing new particle formation data for carbon dioxide over a uniquely broad range of conditions. Our experimental data together with a kinetic model using rate constants from high-level quantum chemical calculations provide a comprehensive picture of new particle formation as nucleation transitions from a barrier-dominated process to the collisional limit.

Heterogeneous Nucleation of Butanol on NaCl: A Computational Study of Temperature, Humidity, Seed Charge, and Seed Size Effects.

Using a combination of quantum chemistry and cluster size distribution dynamics, we study the heterogeneous nucleation of n-butanol and water onto sodium chloride (NaCl)10 seeds at different butanol saturation ratios and relative humidities. We also investigate how the heterogeneous nucleation of butanol is affected by the seed size through comparing (NaCl)5, (NaCl)10, and (NaCl)25 seeds and by seed electrical charge through comparing (Na10Cl9)+, (NaCl)10, and (Na9Cl10)- seeds. Butanol is a common working fluid for condensation particle counters used in atmospheric aerosol studies, and NaCl seeds are frequently used for calibration purposes and as model systems, for example, sea spray aerosol. In general, our simulations reproduce the experimentally observed trends for the NaCl-BuOH-H2O system, such as the increase of nucleation rate with relative humidity and with temperature (at constant supersaturation of butanol). Our results also provide molecular-level insights into the vapor-seed interactions driving the first steps of the heterogeneous nucleation process. The main purpose of this work is to show that theoretical studies can provide molecular understanding of initial steps of heterogeneous nucleation and that it is possible to find cost-effective yet accurate-enough combinations of methods for configurational sampling and energy evaluation to successfully model heterogeneous nucleation of multicomponent systems. In the future, we anticipate that such simulations can also be extended to chemically more complex seeds.

Comparing Reaction Routes for 3(RO···OR') Intermediates Formed in Peroxy Radical Self- and Cross-Reactions.

Organic peroxy radicals (RO2) are key intermediates in the chemistry of the atmosphere. One of the main sink reactions of RO2 is the recombination reaction RO2 + R'O2, which has three main channels (all with O2 as a coproduct): (1) R-H═O + R'OH, (2) RO + R'O, and (3) ROOR'. The RO + R'O "alkoxy" channel promotes radical and oxidant recycling, while the ROOR' "dimer" channel leads to low-volatility products relevant to aerosol processes. The ROOR' channel has only recently been discovered to play a role in the gas phase. Recent computational studies indicate that all of these channels first go through an intermediate complex 1(RO···3O2···OR'). Here, 3O2 is very weakly bound and will likely evaporate from the system, giving a triplet cluster of two alkoxy radicals: 3(RO···OR'). In this study, we systematically investigate the three reaction channels for an atmospherically representative set of RO + R'O radicals formed in the corresponding RO2 + R'O2 reaction. First, we systematically sample the possible conformations of the RO···OR' clusters on the triplet potential energy surface. Next, we compute energetic parameters and attempt to estimate reaction rate coefficients for the three channels: evaporation/dissociation to RO + R'O, a hydrogen shift leading to the formation of R'-H═O + ROH, and "spin-flip" (intersystem crossing) leading to, or at least allowing, the formation of ROOR' dimers. While large uncertainties in the computed energetics prevent a quantitative comparison of reaction rates, all three channels were found to be very fast (with typical rates greater than 106 s-1). This qualitatively demonstrates that the computationally proposed novel RO2 + R'O2 reaction mechanism is compatible with experimental data showing non-negligible branching ratios for all three channels, at least for sufficiently complex RO2.

Impact of Quantum Chemistry Parameter Choices and Cluster Distribution Model Settings on Modeled Atmospheric Particle Formation Rates.

We tested the influence of various parameters on the new particle formation rate predicted for the sulfuric acid-ammonia system using quantum chemistry and cluster distribution dynamics simulations, in our case, Atmospheric Cluster Dynamics Code (ACDC). We found that consistent consideration of the rotational symmetry number of monomers (sulfuric acid and ammonia molecules, and bisulfate and ammonium ions) leads to a significant rise in the predicted particle formation rate, whereas inclusion of the rotational symmetry number of the clusters only changes the results slightly, and only in conditions where charged clusters dominate the particle formation rate. This is because most of the clusters stable enough to participate in new particle formation have a rotational symmetry number of 1, and few exceptions to this rule are positively charged clusters. In contrast, the application of the quasi-harmonic correction for low-frequency vibrational modes tends to generally decrease predicted new particle formation rates and also significantly alters the slope of the formation rate curve plotted against the sulfuric acid concentration, which is a typical convention in atmospheric aerosol science. The impact of the maximum size of the clusters explicitly included in the simulations depends on the simulated conditions. The errors arising from a limited set of clusters are higher for higher evaporation rates, and thus tend to increase with temperature. Similarly, the errors tend to be higher for lower vapor concentrations. The boundary conditions for outgrowing clusters (that are counted as formed particles) have only a small influence on the results, provided that the definition is chemically reasonable and that the set of simulated clusters is sufficiently large. A comparison with data from the Cosmics Leaving OUtdoor Droplets (CLOUD) chamber and a cluster distribution dynamics model using older quantum chemistry input data shows improved agreement when using our new input data and the proposed combination of symmetry and quasi-harmonic corrections.

Hydration of Atmospheric Molecular Clusters III: Procedure for Efficient Free Energy Surface Exploration of Large Hydrated Clusters.

Sampling the shallow free energy surface of hydrated atmospheric molecular clusters is a significant challenge. Using computational methods, we present an efficient approach to obtain minimum free energy structures for large hydrated clusters of atmospheric relevance. We study clusters consisting of two to four sulfuric acid (sa) molecules and hydrate them with up to five water (w) molecules. The structures of the "dry" clusters are obtained using the ABCluster program to yield a large pool of low-lying conformer minima with respect to free energy. The conformers (up to ten) lowest in free energy are then hydrated using our recently developed systematic hydrate sampling technique. Using this approach, we identify a total of 1145 unique (sa)2-4(w)1-5 cluster structures. The cluster geometries and thermochemical parameters are calculated at the ωB97X-D/6-31++G(d,p) level of theory, at 298.15 K and 1 atm. The single-point energy of the most stable clusters is calculated using a high-level DLPNO-CCSD(T0)/aug-cc-pVTZ method. Using the thermochemical data, we calculate the equilibrium hydrate distribution of the clusters under atmospheric conditions and find that the larger (sa)3 and (sa)4 clusters are significantly more hydrated than the smaller (sa)2 cluster or the sulfuric acid (sa)1 molecule. These findings indicate that more than five water molecules might be required to fully saturate the sulfuric acid clusters with water under atmospheric conditions. The presented methodology gives modelers a tool to take the effect of water explicitly into account in atmospheric particle formation models based on quantum chemistry.

Ionization energies in solution with the QM:QM approach.

We discuss a fragment-based QM:QM scheme as a practical way to access the energetics of vertical electronic processes in the condensed phase. In the QM:QM scheme, we decompose the large molecular system into small fragments, which interact solely electrostatically. The energies of the fragments are calculated in a self-consistent field generated by the other fragments and the total energy of the system is calculated as a sum of the fragment energies. We show on two test cases (cytosine and a sodium cation) that the method allows one to accurately simulate the shift of vertical ionization energies (VIE) while going from the gas phase to the bulk. For both examples, the predicted solvent shifts and peak widths estimated at the DFT level agree well with the experimental observations. We argue that the QM:QM approach is more suitable than either an electrostatic embedding based QM/MM approach, a full quantum description at the DFT level with a generally used functional or a combination of both. We also discuss the potential scope of the applicability for other electronic processes such as Auger decay.

Computational Study of the Effect of Mineral Dust on Secondary Organic Aerosol Formation by Accretion Reactions of Closed-Shell Organic Compounds.

The effect of dust aerosols on accretion reactions of water, formaldehyde, and formic acid was studied in the conditions of earth's troposphere at the DLPNO-CCSD(T)/aug-cc-pVTZ//ωB97X-D/6-31++G** level of theory. A detailed analysis of the reaction mechanisms in the gas phase and on the surface of mineral dust, represented by mono- and trisilicic acid, revealed that mineral dust has the potential of decreasing reaction barrier heights. Specifically, at 0 K, mineral dust can lower the apparent energy barrier of the reaction of formaldehyde with formic acid to zero. However, when the entropic contributions to the reaction free energies were accounted for, mineral dust was found to selectively enhance the reaction of water with formaldehyde, while inhibiting the reaction of formaldehyde and formic acid, in the lower parts of the troposphere (with temperatures around 298 K). In the upper troposphere (with temperatures closer to 198 K), mineral dust catalyzes both reactions and also the reaction of methanol with formic acid. Despite the intrinsic potential of mineral dust, calculation of the catalytic enhancement parameter for a likely range of dust aerosol concentrations suggested that dust aerosols will not contribute to secondary organic aerosol formation via dimerization of closed-shell organic compounds. The main reason for this is the relatively low absolute concentration of tropospheric dust aerosol and its inefficiency in increasing the effective reaction rate coefficients.

Intersystem Crossings Drive Atmospheric Gas-Phase Dimer Formation.

High molecular weight "ROOR'" dimers, likely formed in the gas phase through self- and cross-reactions of complex peroxy radicals (RO2), have been suggested to play a key role in forming ultrafine aerosol particles in the atmosphere. However, the molecular-level reaction mechanism producing these dimers remains unknown. Using multireference quantum chemical methods, we explore one potentially competitive pathway for ROOR' production, involving the initial formation of triplet alkoxy radical (RO) pairs, followed by extremely rapid intersystem crossings (ISC) to the singlet surface, permitting subsequent recombination to ROOR'. Using CH3OO + CH3OO as a model system, we show that the initial steps of this reaction mechanism are likely to be very fast, as the transition states for both the formation and the decomposition of the CH3O4CH3 tetroxide intermediate are far below the reactants in energy. Next, we compute ISC rates for seven different atmospherically relevant 3(RO···R'O) complexes. The ISC rates vary significantly depending on the conformation of the complex and also exhibit strong stereoselectivity. Furthermore, the fastest ISC process is usually not between the lowest-energy triplet and singlet states but between the triplet ground state and an exited singlet state. For each studied (RO···R'O) system, at least one low-energy conformer with an ISC rate above 108 s-1 can be found. This demonstrates that gas-phase dimer formation in the atmosphere very likely involves ISCs originating in relativistic quantum mechanics.