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Knattrup Y, Kubečka J, Wu H, Jensen F, Elm J. Reparameterization of GFN1-xTB for atmospheric molecular clusters: applications to multi-acid-multi-base systems. RSC Adv 2024; 14:20048-20055. [PMID: 38911834 PMCID: PMC11191700 DOI: 10.1039/d4ra03021d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 06/16/2024] [Indexed: 06/25/2024] Open
Abstract
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.
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Affiliation(s)
- Yosef Knattrup
- Department of Chemistry, Aarhus University Langelandsgade 140, Aarhus C 8000 Denmark +45 28938085
| | - Jakub Kubečka
- Department of Chemistry, Aarhus University Langelandsgade 140, Aarhus C 8000 Denmark +45 28938085
| | - Haide Wu
- Department of Chemistry, Aarhus University Langelandsgade 140, Aarhus C 8000 Denmark +45 28938085
| | - Frank Jensen
- Department of Chemistry, Aarhus University Langelandsgade 140, Aarhus C 8000 Denmark +45 28938085
| | - Jonas Elm
- Department of Chemistry, Aarhus University Langelandsgade 140, Aarhus C 8000 Denmark +45 28938085
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Chen J, Lane JR, Bates KH, Kjaergaard HG. Atmospheric Gas-Phase Formation of Methanesulfonic Acid. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:21168-21177. [PMID: 38051922 DOI: 10.1021/acs.est.3c07120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
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.
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Affiliation(s)
- Jing Chen
- Department of Chemistry, University of Copenhagen, Universitetsparken 5, Copenhagen Ø DK-2100, Denmark
| | - Joseph R Lane
- School of Science, University of Waikato, Private Bag 3105, Hamilton 3240, New Zealand
| | - Kelvin H Bates
- NOAA Chemical Sciences Laboratory, Earth System Research Laboratories & Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado 80305, United States
| | - Henrik G Kjaergaard
- Department of Chemistry, University of Copenhagen, Universitetsparken 5, Copenhagen Ø DK-2100, Denmark
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Kubečka J, Besel V, Neefjes I, Knattrup Y, Kurtén T, Vehkamäki H, Elm J. Computational Tools for Handling Molecular Clusters: Configurational Sampling, Storage, Analysis, and Machine Learning. ACS OMEGA 2023; 8:45115-45128. [PMID: 38046354 PMCID: PMC10688175 DOI: 10.1021/acsomega.3c07412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 10/25/2023] [Accepted: 10/26/2023] [Indexed: 12/05/2023]
Abstract
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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Affiliation(s)
- Jakub Kubečka
- Aarhus
University, Department of Chemistry, Langelandsgade 140, Aarhus 8000, Denmark
| | - Vitus Besel
- University
of Helsinki, Institute for Atmospheric and
Earth System Research/Physics, Faculty of Science, P.O. Box 64, Helsinki 00140, Finland
| | - Ivo Neefjes
- University
of Helsinki, Institute for Atmospheric and
Earth System Research/Physics, Faculty of Science, P.O. Box 64, Helsinki 00140, Finland
| | - Yosef Knattrup
- Aarhus
University, Department of Chemistry, Langelandsgade 140, Aarhus 8000, Denmark
| | - Theo Kurtén
- University
of Helsinki, Institute for Atmospheric and
Earth System Research/Chemistry, Faculty of Science, P.O. Box 64, Helsinki 00140, Finland
| | - Hanna Vehkamäki
- University
of Helsinki, Institute for Atmospheric and
Earth System Research/Physics, Faculty of Science, P.O. Box 64, Helsinki 00140, Finland
| | - Jonas Elm
- Aarhus
University, Department of Chemistry, Langelandsgade 140, Aarhus 8000, Denmark
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