Absolute Saturation Vapor Pressures of Three Fatty Acid Methyl Esters around Room Temperature.

We report measurements of absolute saturation vapor pressures around room temperature for three fatty acid methyl esters (methyl octanoate, methyl decanoate, and methyl dodecanoate) using a recently developed experimental method in which the saturation vapor pressures are determined from the vaporization dynamics of a cooled sample during thermalization to a higher chamber temperature.

Modeling Local Aerosol Surface Environments: Clustering of Pyruvic Acid Analogs, Water, and Na, Cl Ions.

Pyruvic acid is an omnipresent compound in nature and is found both in the gas phase and in the particle phase of the atmosphere as well as in aqueous solution in the hydrosphere. Despite much literature on the photochemical degradation and stability of pyruvic acid in different chemical environments, the study of simultaneous interactions between gas-phase pyruvic acid or similar carboxylic acids with water and ions is not well-understood. Here, we present a study of microhydrated molecular clusters containing pyruvic acid and the structurally analogous carboxylic acids lactic acid, propionic acid, and 2,2-dihydroxypropanoic acid by probing geometries, binding free energies, hydrate distributions, as well as their infrared (IR) absorption spectra.

Massive Assessment of the Geometries of Atmospheric Molecular Clusters.

Atmospheric molecular clusters are important for the formation of new aerosol particles in the air. However, current experimental techniques are not able to yield direct insight into the cluster geometries. This implies that to date there is limited information about how accurately the applied computational methods depict the cluster structures.

Accurate modeling of the potential energy surface of atmospheric molecular clusters boosted by neural networks.

The computational cost of accurate quantum chemistry (QC) calculations of large molecular systems can often be unbearably high. Machine learning offers a lower computational cost compared to QC methods while maintaining their accuracy. In this study, we employ the polarizable atom interaction neural network (PaiNN) architecture to train and model the potential energy surface of molecular clusters relevant to atmospheric new particle formation, such as sulfuric acid-ammonia clusters.

Iodine Clusters in the Atmosphere I: Computational Benchmark and Dimer Formation of Oxyacids and Oxides.

The contribution of iodine-containing compounds to atmospheric new particle formation is still not fully understood, but iodic acid and iodous acid are thought to be significant contributors. While several quantum chemical studies have been carried out on clusters containing iodine, there is no comprehensive benchmark study quantifying the accuracy of the applied methods. Here, we present the first study in a series that investigate the role of iodine species in atmospheric cluster formation.

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.

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.

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.

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

Iodine oxoacids (HIO and HIO)-driven nucleation has been suggested to efficiently contribute to new particle formation (NPF) in marine atmospheres. Abundant atmospheric nucleation precursors may further enhance HIO-HIO-driven nucleation through various multicomponent nucleation mechanisms. However, the specific enhancing potential (EP) of different precursors remains largely unknown.

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.

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.

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)(AM) clusters with up to 30 and the (SA)(AM) clusters, with = 6-20. The cluster configurations are sampled using the ABCluster program, and the cluster geometries and thermochemical parameters are calculated using GFN1-xTB.

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).

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.

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.

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)(FA)(base) 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.

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.

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.

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

The formation of secondary organic aerosol (SOA) from the structurally similar monoterpenes, α-pinene and Δ-carene, differs substantially. The aerosol phase is already complex for a single precursor, and when mixtures are oxidized, products, e.g.

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)(MSA)(DMA) ( + ≤ 4 and ≤ 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(T)/aug-cc-pVTZ calculations.

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)(base) and (acid)(base) 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.