Clusteromics II: Methanesulfonic Acid-Base Cluster Formation

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.

Implications for new particle formation in air of the use of monoethanolamine in carbon capture and storage

Alkanolamines are currently being deployed in carbon capture and storage (CCS) technology worldwide, and atmospheric emissions have been found to coincide with locations exhibiting elevated concentrations of methanesulfonic acid (MSA). It is thus critical to understand the fate and potential atmospheric reactions of these chemicals. This study reports the characterization of sub-10 nm nanoparticles produced through the acid-base reaction between gas phase monoethanolamine (MEA) and MSA, a product of organosulfur compound oxidation in air, using a flow reactor under dry and humid (up to ∼60% RH) conditions. Number size distribution measurements show that MEA is even more efficient than methylamine in forming nanoparticles on reaction with MSA. This is attributed to the fact that the MEA structure contains both an -NH2 and an -OH group that facilitate hydrogen bonding within the clusters, in addition to the electrostatic interactions. Due to this already strong H-bond network, water has a relatively small influence on new particle formation (NPF) and growth in this system, in contrast to MSA reactions with alkylamines. Acid/base molar ratios of unity for 4-12 nm particles were measured using thermal desorption chemical ionization mass spectrometry. The data indicate that reaction of MEA with MSA may dominate NPF under some atmospheric conditions. Thus, the unique characteristics of alkanolamines in NPF must be taken into account for accurate predictions of impacts of CCS on visibility, health and climate.

Atmospheric Gas-Phase Formation of Methanesulfonic Acid

Despite its impact on the climate, the mechanism of methanesulfonic acid (MSA) formation in the oxidation of dimethyl sulfide (DMS) remains unclear. The DMS + OH reaction is known to form methanesulfinic acid (MSIA), methane sulfenic acid (MSEA), the methylthio radical (CH3S), and hydroperoxymethyl thioformate (HPMTF). Among them, HPMTF reacts further to form SO2 and OCS, while the other three form the CH3SO2 radical. Based on theoretical calculations, we find that the CH3SO2 radical can add O2 to form CH3S(O)2OO, which can react further to form MSA. The branching ratio is highly temperature sensitive, and the MSA yield increases with decreasing temperature. In warmer regions, SO2 is the dominant product of DMS oxidation, while in colder regions, large amounts of MSA can form. Global modeling indicates that the proposed temperature-sensitive MSA formation mechanism leads to a substantial increase in the simulated global atmospheric MSA formation and burden.

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.

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.

Role of Methanesulfonic Acid in Sulfuric Acid-Amine and Ammonia New Particle Formation

Aerosol nucleation accounts for over half of all seed particles for cloud droplet formation. In the atmosphere, sulfuric acid (SA) nucleates with ammonia, amines, oxidized organics, and many more compounds to form particles. Studies have also shown that methanesulfonic acid (MSA) nucleates independently with amines and ammonia. MSA and SA are produced simultaneously via dimethyl sulfide oxidation in the marine atmosphere. However, limited knowledge exists on how MSA and SA nucleate together in the presence of various atmospherically relevant base compounds, which is critical to predicting marine nucleation rates accurately. This work provides experimental evidence that SA and MSA react to form particles with amines and that the SA-MSA-base nucleation has different reaction pathways than SA-base nucleation. Specifically, the formation of the SA-MSA heterodimer creates more energetically favorable pathways for SA-MSA-methylamine nucleation and an enhancement of nucleation rates. However, SA-trimethylamine nucleation is suppressed by MSA, likely due to the steric hindrance of the MSA and trimethylamine. These results display the importance of including nucleation reactions between SA, MSA, and various amines to predict particle nucleation rates in the marine atmosphere.

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.

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.

Computational chemistry of cluster: Understanding the mechanism of atmospheric new particle formation at the molecular level

New particle formation (NPF), which exerts significant influence over human health and global climate, has been a hot topic and rapidly expands field of research in the environmental and atmospheric chemistry recent years. Generally, NPF contains two processes: formation of critical nucleus and further growth of the nucleus. However, due to the complexity of the atmospheric nucleation, which is a multicomponent process, formation of critical clusters as well as their growth is still connected to large uncertainties. Detection limits of instruments in measuring specific gaseous aerosol precursors and chemical compositions at the molecular level call for computational studies. Computational chemistry could effectively compensate the deficiency of laboratory experiments as well as observations and predict the nucleation mechanisms. We review the present theoretical literatures that discuss nucleation mechanism of atmospheric clusters. Focus of this review is on different nucleation systems involving sulfur-containing species, nitrogen-containing species and iodine-containing species. We hope this review will provide a deep insight for the molecular interaction of nucleation precursors and reveal nucleation mechanism at the molecular level.

The driving effects of common atmospheric molecules for formation of prenucleation clusters: the case of sulfuric acid, formic acid, nitric acid, ammonia, and dimethyl amine

How secondary aerosols form is critical as aerosols' impact on Earth's climate is one of the main sources of uncertainty for understanding global warming. The beginning stages for formation of prenucleation complexes, that lead to larger aerosols, are difficult to decipher experimentally. We present a computational chemistry study of the interactions between three different acid molecules and two different bases. By combining a comprehensive search routine covering many thousands of configurations at the semiempirical level with high level quantum chemical calculations of approximately 1000 clusters for every possible combination of clusters containing a sulfuric acid molecule, a formic acid molecule, a nitric acid molecule, an ammonia molecule, a dimethylamine molecule, and 0-5 water molecules, we have completed an exhaustive search of the DLPNO-CCSD(T)/CBS//ωB97X-D/6-31++G** Gibbs free energy surface for this system. We find that the detailed geometries of each minimum free energy cluster are often more important than traditional acid or base strength. Addition of a water molecule to a dry cluster can enhance stabilization, and we find that the (SA)(NA)(A)(DMA)(W) cluster has special stability. Equilibrium calculations of SA, FA, NA, A, DMA, and water using our quantum chemical ΔG° values for cluster formation and realistic estimates of the concentrations of these monomers in the atmosphere reveals that nitric acid can drive early stages of particle formation just as efficiently as sulfuric acid. Our results lead us to believe that particle formation in the atmosphere results from the combination of many different molecules that are able to form highly stable complexes with acid molecules such as SA, NA, and FA.

Critical Role of Iodous Acid in Neutral Iodine Oxoacid Nucleation

Nucleation of neutral iodine particles has recently been found to involve both iodic acid (HIO₃) and iodous acid (HIO₂). However, the precise role of HIO₂ in iodine oxoacid nucleation remains unclear. Herein, we probe such a role by investigating the cluster formation mechanisms and kinetics of (HIO₃)_m(HIO₂)_n (m = 0-4, n = 0-4) clusters with quantum chemical calculations and atmospheric cluster dynamics modeling. When compared with HIO₃, we find that HIO₂ binds more strongly with HIO₃ and also more strongly with HIO₂. After accounting for ambient vapor concentrations, the fastest nucleation rate is predicted for mixed HIO₃-HIO₂ clusters rather than for pure HIO₃ or HIO₂ ones. Our calculations reveal that the strong binding results from HIO₂ exhibiting a base behavior (accepting a proton from HIO₃) and forming stronger halogen bonds. Moreover, the binding energies of (HIO₃)_m(HIO₂)_n clusters show a far more tolerant choice of growth paths when compared with the strict stoichiometry required for sulfuric acid-base nucleation. Our predicted cluster formation rates and dimer concentrations are acceptably consistent with those measured by the Cosmic Leaving Outdoor Droplets (CLOUD) experiment. This study suggests that HIO₂ could facilitate the nucleation of other acids beyond HIO₃ in regions where base vapors such as ammonia or amines are scarce.

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.

Amine-Enhanced Methanesulfonic Acid-Driven Nucleation: Predictive Model and Cluster Formation Mechanism

Atmospheric amines are considered to be an effective enhancer for methanesulfonic acid (MSA)-driven nucleation. However, out of the 195 detected atmospheric amines, the enhancing potential (EP) has so far only been studied for five amines. This severely hinders the understanding of the contribution of amines to MSA-driven nucleation. Herein, a two-step procedure was employed to probe the EP of various amines on MSA-driven nucleation. Initially, the formation free energies (ΔG) of 50 MSA-amine dimer clusters were calculated. Based on the calculated ΔG values, a robust quantitative structure-activity relationship (QSAR) model was built and utilized to predict the ΔG values of the remaining 145 amines. The QSAR model identified two guanidino-containing compounds as the potentially strongest enhancer for MSA-driven nucleation. Second, the EP of guanidino-containing compounds was studied by employing larger clusters and selecting guanidine (Gud) as a representative. The results indicate that Gud indeed has the strongest EP. The Gud-MSA system presents a unique clustering mechanism, proceeding via the initial formation of the (Gud)₁(MSA)₁ cluster, and subsequently by cluster collisions with either a (Gud)₁(MSA)₁ or (Gud)₂(MSA)₂ cluster. The developed QSAR model and the identification of amines with the strongest EP provide a foundation for comprehensively evaluating the contribution of atmospheric amines to MSA-driven nucleation.

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.

Monomers of Glycine and Serine Have a Limited Ability to Hydrate in the Atmosphere

The role of atmospheric aerosols on climate change is one of the biggest uncertainties in most global climate models. Organic aerosols have been identified as potential cloud condensation nuclei (CCN), and amino acids are organic molecules that could serve as CCN. Amino acids make up a significant portion of the total organic material in the atmosphere, and herein we present a systematic study of hydration for two of the most common atmospheric amino acids, glycine and serine. We compute DLPNO/CCSD(T)//M08-HX/MG3S thermodynamic properties and atmospheric concentrations of Gly(H₂O)_n and Ser(H₂O)_n, where n = 1-5. We predict that serine-water clusters have higher concentrations at n = 1 and 5, while glycine-water clusters have higher concentrations at n = 2-4. However, both glycine and serine are inferred to exist primarily in their nonhydrated monomer forms in the absence of other species such as sulfuric acid.

Tri-Base Synergy in Sulfuric Acid-Base Clusters

Synergistic effects between different bases can greatly enhance atmospheric sulfuric acid (SA)-base cluster formation. However, only the synergy between two base components has previously been investigated. Here, we extend this concept to three bases by studying large atmospherically relevant (SA)3(base)3 clusters, with the bases ammonia (A), methylamine (MA), dimethylamine (DMA), trimethylamine (TMA) and ethylenediamine (EDA). Using density functional theory—ωB97X-D/6-31++G(d,p)—we calculate the cluster structures and vibrational frequencies. The thermochemical parameters are calculated at 29,815 K and 1 atm, using the quasi-harmonic approximation. The binding energies of the clusters are calculated using high level DLPNO-CCSD(T0)/aug-cc-pVTZ. We find that the cluster stability in general depends on the basicity of the constituent bases, with some noteworthy additional guidelines: DMA enhances the cluster stability, TMA decreases the cluster stability and there is high synergy between DMA and EDA. Based on our calculations, we find it highly likely that three, or potentially more, different bases, are involved in the growth pathways of sulfuric acid-base clusters.