ArticleACS omega2023
Clusterome: A Comprehensive Data Set of Atmospheric Molecular Clusters for Machine Learning Applications.
Article in ACS omega, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 17 citations in OpenAlex.
- Coupled Cluster Free Energies for Atmospheric Molecular Clusters: Benchmark and Matching Experimental Free Energies.ACS omega · 2026Article
- Fast and Interpretable Machine Learning Modeling of Atmospheric Molecular Clusters.The journal of physical chemistry. A · 2026Article
- Extrapolating Local Coupled Cluster Calculations toward CCSD(T)/CBS Binding Energies of Atmospheric Molecular Clusters.ACS omega · 2025Article
- Article
- Accurate modeling of the potential energy surface of atmospheric molecular clusters boosted by neural networks.Environmental science. Advances · 2024Article
- Reparameterization of GFN1-xTB for atmospheric molecular clusters: applications to multi-acid-multi-base systems.RSC advances · 2024Article
- Improved Configurational Sampling Protocol for Large Atmospheric Molecular Clusters.ACS omega · 2023Article
- Computational Tools for Handling Molecular Clusters: Configurational Sampling, Storage, Analysis, and Machine Learning.ACS omega · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.