Clusterome: A Comprehensive Data Set of Atmospheric Molecular Clusters for Machine Learning Applications

Knattrup, Yosef; Kubečka, Jakub; Ayoubi, Daniel; Elm, Jonas

doi:10.1021/acsomega.3c02203

Cited by 8 publications

(5 citation statements)

References 54 publications

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“…Hence, based on these findings, if a semiempirical method should be used in the funneling approach, GFN1-xTB is a better choice than GFN2-xTB. This is consistent with recent benchmark studies 22 , 23 , 51 , 52 and shows that the trend follows here. The empirically corrected DFT methods, PBEh-3c, B97-3c, and r 2 SCAN-3c, perform well with RMSDs of 0.18, 0.11, and 0.09 Å, respectively.…”

Section: Resultssupporting

confidence: 94%

Improved Configurational Sampling Protocol for Large Atmospheric Molecular Clusters

Wu,

Engsvang,

Knattrup

et al. 2023

ACS Omega

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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.

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Section: Resultssupporting

confidence: 94%

Improved Configurational Sampling Protocol for Large Atmospheric Molecular Clusters

Wu,

Engsvang,

Knattrup

et al. 2023

ACS Omega

Self Cite

View full text Add to dashboard Cite

show abstract

“…Hence, based on these findings, if a semi-empirical method should be used in the funneling approach, GFN1-xTB is a better choice than GFN2-xTB. This is consistent with recent benchmark studies 22,23,49,50 and shows that the trend follows here. The empirically corrected DFT methods PBEh-3c, B97-3c, and r 2 SCAN-3c perform well with RMSDs of 0.18, 0.11, and 0.09 Å, respectively.…”

Section: Cluster Geometriessupporting

confidence: 93%

Improved Configurational Sampling Protocol for Large Atmospheric Molecular Clusters

Wu,

Engsvang,

Knattrup

et al. 2023

Preprint

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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 com- promise between 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 (approx. 2 nm in diameter) and propose 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 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.

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“…In our recent Clusterome paper, 123 we presented a large (∼250k), multiacid–multibase, atmospherically relevant, molecular cluster database (available in the ACDB 2.0 repository, see the Supporting Information ). Here, we extract only ∼32k structures of the (H 2 SO 4 –SA) 0–2 (bases) 0–2 clusters (termed Clusteromics I), where bases correspond to ammonia (AM), methylamine (MA), dimethylamine (DMA), trimethylamine (TMA), and ethylenediamine (EDA).…”

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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Clusterome: A Comprehensive Data Set of Atmospheric Molecular Clusters for Machine Learning Applications

Cited by 8 publications

References 54 publications

Improved Configurational Sampling Protocol for Large Atmospheric Molecular Clusters

Improved Configurational Sampling Protocol for Large Atmospheric Molecular Clusters

Improved Configurational Sampling Protocol for Large Atmospheric Molecular Clusters

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

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