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.

A new setup for measurements of absolute saturation vapor pressures using a dynamical method: Experimental concept and validation.

We describe a new experimental system for direct measurements of the absolute saturation vapor pressures of liquid or solid samples. The setup allows the isolation of the sample under steady conditions in an ultra-high vacuum chamber, where the measurement of the sample's vapor pressure as a function of its temperature can be performed in a range around room temperature and in a pressure range defined only by the applied absolute pressure sensor. We characterize the setup and illustrate its capability to measure saturation vapor pressures as well as enthalpies of evaporation around room temperature with explicit measurements on four liquid compounds (diethyl phthalate, 1-decanol, 1-heptanol, and 1-hexanol) for which accurate vapor pressures have previously been reported.

Natural Marine Precursors Boost Continental New Particle Formation and Production of Cloud Condensation Nuclei.

Marine dimethyl sulfide (DMS) emissions are the dominant source of natural sulfur in the atmosphere. DMS oxidizes to produce low-volatility acids that potentially nucleate to form particles that may grow into climatically important cloud condensation nuclei (CCN). In this work, we utilize the chemistry transport model ADCHEM to demonstrate that DMS emissions are likely to contribute to the majority of CCN during the biological active period (May-August) at three different forest stations in the Nordic countries. DMS increases CCN concentrations by forming nucleation and Aitken mode particles over the ocean and land, which eventually grow into the accumulation mode by condensation of low-volatility organic compounds from continental vegetation. Our findings provide a new understanding of the exchange of marine precursors between the ocean and land, highlighting their influence as one of the dominant sources of CCN particles over the boreal forest.

HIO3-HIO2-Driven Three-Component Nucleation: Screening Model and Cluster Formation Mechanism.

Iodine oxoacids (HIO3 and HIO2)-driven nucleation has been suggested to efficiently contribute to new particle formation (NPF) in marine atmospheres. Abundant atmospheric nucleation precursors may further enhance HIO3-HIO2-driven nucleation through various multicomponent nucleation mechanisms. However, the specific enhancing potential (EP) of different precursors remains largely unknown. Herein, the EP-based screening model of precursors and enhancing mechanism of the precursor with the highest EP on HIO3-HIO2 nucleation were investigated. The formation free energies (ΔG), as critical parameters for evaluating EP, were calculated for the dimers of 63 selected precursors with HIO2. Based on the ΔG values, (1) a quantitative structure-activity relationship model was developed for evaluating ΔG of other precursors and (2) atmospheric concentrations of 63 (precursor)1(HIO2)1 dimer clusters were assessed to identify the precursors with the highest EP for HIO3-HIO2-driven nucleation by combining with earlier results for the nucleation with HIO3 as the partner. Methanesulfonic acid (MSA) was found to be one of the precursors with the highest EP. Finally, we found that MSA can effectively enhance HIO3-HIO2 nucleation at atmospheric conditions by studying larger MSA-HIO3-HIO2 clusters. These results augment our current understanding of HIO3-HIO2 and MSA-driven nucleation and may suggest a larger impact of HIO2 in atmospheric aerosol nucleation.

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.

An overlooked oxidation mechanism of toluene: computational predictions and experimental validations.

Secondary organic aerosols (SOAs) influence the Earth's climate and threaten human health. Aromatic hydrocarbons (AHs) are major precursors for SOA formation in the urban atmosphere. However, the revealed oxidation mechanism dramatically underestimates the contribution of AHs to SOA formation, strongly suggesting the importance of seeking additional oxidation pathways for SOA formation. Using toluene, the most abundant AHs, as a model system and the combination of quantum chemical method and field observations based on advanced mass spectrometry, we herein demonstrate that the second-generation oxidation of AHs can form novel epoxides (TEPOX) with high yield. Such TEPOX can further react with H2SO4 or HNO3 in the aerosol phase to form less-volatile compounds including novel non-aromatic and ring-retaining organosulfates or organonitrates through reactive uptakes, providing new candidates of AH-derived organosulfates or organonitrates for future ambient observation. With the newly revealed mechanism, the chemistry-aerosol box modeling revealed that the SOA yield of toluene oxidation can reach up to 0.35, much higher than 0.088 based on the original mechanism under the conditions of pH = 2 and 0.1 ppbv NO. This study opens a route for the formation of reactive uptake SOA precursors from AHs and significantly fills the current knowledge gap for SOA formation in the urban atmosphere.

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.

Enhancement of Atmospheric Nucleation Precursors on Iodic Acid-Induced Nucleation: Predictive Model and Mechanism.

Iodic acid (IA) has recently been recognized as a key driver for new particle formation (NPF) in marine atmospheres. However, the knowledge of which atmospheric vapors can enhance IA-induced NPF remains limited. The unique halogen bond (XB)-forming capacity of IA makes it difficult to evaluate the enhancing potential (EP) of target compounds on IA-induced NPF based on widely studied sulfuric acid systems. Herein, we employed a three-step procedure to evaluate the EP of potential atmospheric nucleation precursors on IA-induced NPF. First, we evaluated the EP of 63 precursors by simulating the formation free energies (ΔG) of the IA-containing dimer clusters. Among all dimer clusters, 44 contained XBs, demonstrating that XBs are frequently formed. Based on the calculated ΔG values, a quantitative structure-activity relationship model was developed for evaluating the EP of other precursors. Second, amines and O/S-atom-containing acids were found to have high EP, with diethylamine (DEA) yielding the highest potential to enhance IA-induced nucleation by combining both the calculated ΔG and atmospheric concentration of considered 63 precursors. Finally, by studying larger (IA)1-3(DEA)1-3 clusters, we found that the IA-DEA system with merely 0.1 ppt (2.5×106 cm-3) DEA yields comparable nucleation rates to that of the IA-iodous acid system.

Clusteromics V: Organic Enhanced Atmospheric Cluster Formation.

Formic acid (FA) is a prominent candidate for organic enhanced nucleation due to its high abundance and stabilizing effect on smaller clusters. Its role in new particle formation is studied through the use of state-of-the-art quantum chemical methods on the cluster systems (acid)1-2(FA)1(base)1-2 with the acids being sulfuric acid (SA)/methanesulfonic acid (MSA) and the bases consisting of ammonia (A), methylamine (MA), dimethylamine (DMA), trimethylamine (TMA), and ethylenediamine (EDA). A funneling approach is used to determine the cluster structures with initial configurations generated through the ABCluster program, followed by semiempirical PM7 and ωB97X-D/6-31++G(d,p) calculations. The final binding free energy is calculated at the DLPNO-CCSD(T0)/aug-cc-pVTZ//ωB97X-D/6-31++G(d,p) level of theory using the quasi-harmonic approximation. Cluster dynamics simulations show that FA has a minuscule or negligible effect on the MSA-FA-base systems as well as most of the SA-FA-base systems. The SA-FA-DMA cluster system shows the highest influence from FA with an enhancement of 21%, compared to its non-FA counterpart.

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.

Ozonolysis of α-Pinene and Δ3-Carene Mixtures: Formation of Dimers with Two Precursors.

The formation of secondary organic aerosol (SOA) from the structurally similar monoterpenes, α-pinene and Δ3-carene, differs substantially. The aerosol phase is already complex for a single precursor, and when mixtures are oxidized, products, e.g., dimers, may form between different volatile organic compounds (VOCs). This work investigates whether differences in SOA formation and properties from the oxidation of individual monoterpenes persist when a mixture of the monoterpenes is oxidized. Ozonolysis of α-pinene, Δ3-carene, and a 1:1 mixture of them was performed in the Aarhus University Research on Aerosol (AURA) atmospheric simulation chamber. Here, ∼100 ppb of monoterpene was oxidized by 200 ppb O3 under dark conditions at 20 °C. The particle number concentration and particle mass concentration for ozonolysis of α-pinene exceed those from ozonolysis of Δ3-carene alone, while their mixture results in concentrations similar to α-pinene ozonolysis. Detailed offline analysis reveals evidence of VOC-cross-product dimers in SOA from ozonolysis of the monoterpene mixture: a VOC-cross-product dimer likely composed of the monomeric units cis-caric acid and 10-hydroxy-pinonic acid and a VOC-cross-product dimer ester likely from the monomeric units caronaldehyde and terpenylic acid were tentatively identified by liquid chromatography-mass spectrometry. To improve the understanding of chemical mechanisms determining SOA, it is relevant to identify VOC-cross-products.

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.

Clusteromics IV: The Role of Nitric Acid in Atmospheric Cluster Formation.

Nitric acid (NA) has previously been shown to affect atmospheric new particle formation; however, its role still remains highly uncertain. Through the employment of state-of-the-art quantum chemical methods, we study the (acid)1-2(base)1-2 and (acid)3(base)2 clusters containing at least one nitric acid (NA) and sulfuric acid (SA) or methanesulfonic acid (MSA) with bases ammonia (A), methylamine (MA), dimethylamine (DMA), trimethylamine (TMA), and ethylenediamine (EDA). The initial cluster configurations are generated using the ABCluster program. PM7 and ωB97X-D/6-31++G(d,p) calculations are used to reduce the number of relevant configurations. The thermochemical parameters are calculated at the ωB97X-D/6-31++G(d,p) level of theory with the quasi-harmonic approximation, and the final single-point energies are calculated with high-level DLPNO-CCSD(T0)/aug-cc-pVTZ calculations. The enhancing effect from the presence of nitric acid on cluster formation is studied using the calculated thermochemical data and cluster dynamics simulations. We find that when NA is in excess compared with the other acids, it has a substantial enhancing effect on the cluster formation potential.

Clusteromics III: Acid Synergy in Sulfuric Acid-Methanesulfonic Acid-Base Cluster Formation.

Acid-base molecular clusters are an important stage in atmospheric new particle formation. While such clusters are most likely multicomponent in nature, there are very few reports on clusters consisting of multiple acid molecules and multiple base molecules. By applying state-of-the-art quantum chemical methods, we herein study electrically neutral (SA)1(MSA)1(base)0-2 clusters with base = ammonia (A), methylamine (MA), dimethylamine (DMA), trimethylamine (TMA) and ethylenediamine (EDA). The cluster structures are obtained using a funneling approach employing the ABCluster program, semiempirical PM7 calculations and ωB97X-D/6-31++G(d,p) calculations. The final binding free energies are calculated at the DLPNO-CCSD(T0)/aug-cc-pVTZ//ωB97X-D/6-31++G(d,p) level of theory using the quasi-harmonic approximation. Based on the calculated cluster geometries and thermochemistry (at 298.15 K and 1 atm), we find that the mixed (SA)1(MSA)1(base)1-2 clusters more resemble the (SA)2(base)1-2 clusters compared to the (MSA)2(base)1-2 clusters. Hence, some of the steric hindrance and lack of hydrogen bond capacity previously observed in the (MSA)2(base)1-2 clusters is diminished in the corresponding (SA)1(MSA)1(base)1-2 clusters. Cluster kinetics simulations reveal that the presence of an MSA molecule in the clusters enhances the cluster formation potential by up to a factor of 20. We find that the SA-MSA-DMA clusters have the highest cluster formation potential, and thus, this system should be further extended to larger sizes in future studies.