Reducing chemical complexity in representation of new-particle formation: evaluation of simplification approaches

Olenius, Tinja; Bergström, R. W.; Kubečka, Jakub; Myllys, Nanna; Elm, Jonas

doi:10.1039/d2ea00174h

Cited by 5 publications

(5 citation statements)

References 52 publications

(79 reference statements)

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“…There is no general solution to this problem. One could potentially lump some monomers together based on similarity or use ML methods to accelerate the CS. , Another option would be to use a lower level of theory along, e.g., the water ( w ) axis.…”

Section: Application and Discussionmentioning

confidence: 99%

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

Kubečka,

Besel,

Neefjes

et al. 2023

ACS Omega

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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.

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Section: Application and Discussionmentioning

confidence: 99%

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

Kubečka,

Besel,

Neefjes

et al. 2023

ACS Omega

Self Cite

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“…catalyst development, [1][2][3] to tailor polymerization, [4][5][6] or to optimize fuels and their combustion, 7,8 and in the field of atmospheric chemistry. 9 These models consist of thermodynamic and kinetic data and potentially further properties like transport data etc. Acquiring data for these models is possible via experiments or, nowadays to an increasing extent, from ab initio calculations, i.e.…”

Section: Introductionmentioning

confidence: 99%

Efficient Generation of Torsional Energy Profiles by Multifidelity Gaussian Processes for Hindered-Rotor Corrections

Fleck,

Kopp,

Viswanathan

et al. 2024

Preprint

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Accurate thermochemistry computations often require a proper treatment of torsional modes. The one-dimensional hindered rotor model has proven to be a computationally efficient solution, given a sufficiently accurate potential energy surface. Methods that provide potential energies at various compromises of uncertainty and computational time demand can be optimally combined within a multifidelity treatment. In this study, we demonstrate how multifidelity modeling leads to: 1. smooth interpolation along low-fidelity scan points with uncertainty estimates, 2. inclusion of high-fidelity data that change the energetic order of conformations, and 3. predict best next-point calculations to extend an initial coarse grid. Our diverse application set comprises molecules, clusters, and transition states of alcohols, ethers, and rings. We discuss limitations for cases where the low-fidelity computation is highly unreliable. Different features of the potential energy curve affect different quantities. To obtain “optimal” fits, we therefore apply strategies ranging from simple minimization of deviations to developing an acquisition function tailored for statistical thermodynamics. Bayesian prediction of best next calculations can save a substantial amount of computation time for one- and multi-dimensional hindered rotors.

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“…To date, a great deal of effort has been expended to identify sources of organic particles, as well as to better understand the impact of atmospheric parameters, such as temperature, that may influence NPF. In addition to advancing our fundamental knowledge of atmospheric chemical processes, quantitative data on the role of atmospheric parameters on NPF are of interest to improve the predictive accuracy of global climate models [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…Atmospheric NPF is initiated by the formation of molecular clusters, often involving atmospheric ions, sulfuric acid, water, and highly oxidized organic molecules (HOMs) [11,[13][14][15][16]; moreover, it has been proposed that the incorporation of HOMs can increase cluster survivability [17][18][19]. Upon cluster formation, a balance exists between losses of these clusters (for example, through processes such as evaporation, condensation, and coagulation) and growth to a critical size that can facilitate atmospheric particle formation via condensation of low-volatility gas-phase products [20].…”

Section: Introductionmentioning

confidence: 99%

Effect of Relative Humidity on the Rate of New Particle Formation for Different VOCs

Flueckiger,

Petrucci

2024

Atmosphere

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Atmospheric new particle formation (NPF) is an important source of aerosol particles and cloud condensation nuclei, which affect both climate and human health. In pristine environments, oxidation of biogenic volatile organic compounds (VOCs) is a major contributor to NPF. However, the impact of relative humidity (RH) on NPF from these precursors remains poorly understood. Herein, we report on NPF, as inferred from measurements of total particle number density with a particle diameter (dp) > 7 nm, from three VOCs (sabinene, α-terpineol, and myrtenol) subjected to dark ozonolysis. From a series of comparative experiments under humid (60% RH) and dry (~0% RH) conditions and a variety of VOC mixing ratios (ξVOC, parts per billion by volume, ppbv), we show varied behavior in NPF at elevated RH depending on the VOC and ξVOC. In general, RH-dependent enhancement of NPF at an ξVOC between <1 ppbv and 20 ppbv was observed for select VOCs. Our results suggest that gaseous water at particle genesis enhances NPF by promoting the formation of low-volatility organic compound gas-phase products (LVOCs). This is supported by measurements of the rate of NPF for α-pinene-derived SOA, where RH had a greater influence on the initial rate of NPF than did ξVOC and ξO3.

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Reducing chemical complexity in representation of new-particle formation: evaluation of simplification approaches

Abstract: Adequate representation of new-particle formation from vapors in aerosol microphysics and atmospheric transport models is essential for providing reliable predictions of ambient particle numbers and for interpretation of observations. Atmospheric...

Cited by 5 publications

References 52 publications

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

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

Efficient Generation of Torsional Energy Profiles by Multifidelity Gaussian Processes for Hindered-Rotor Corrections

Effect of Relative Humidity on the Rate of New Particle Formation for Different VOCs

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