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de Jonge, R. W., Xavier, C., Olenius, T., Elm, J., Svenhag, C., Hyttinen, N., Nieradzik, L., Sarnela, N., Kristensson, A., Petäjä, T., Ehn, M. & Roldin, P. (2024). Natural Marine Precursors Boost Continental New Particle Formation and Production of Cloud Condensation Nuclei. Environmental Science & Technology, 58(25), 10956-10968. https://doi.org/10.1021/acs.est.4c01891

Engsvang, M., Knattrup, Y., Kubečka, J. & Elm, J. (2024). Chlorine Oxyacids Potentially Contribute to Arctic Aerosol Formation. Environmental Science and Technology Letters, 11(2), 101-105. https://doi.org/10.1021/acs.estlett.3c00902

Engsvang, M., Wu, H. & Elm, J. (2024). Iodine Clusters in the Atmosphere I: Computational Benchmark and Dimer Formation of Oxyacids and Oxides. ACS Omega, 9(29), 31521-31532. https://doi.org/10.1021/acsomega.4c01235

Jensen, A. B. & Elm, J. (2024). Massive Assessment of the Geometries of Atmospheric Molecular Clusters. Journal of Chemical Theory and Computation, 20(19), 8549–8558. https://doi.org/10.1021/acs.jctc.4c01046

Knattrup, Y., Kubečka, J., Wu, H., Jensen, F. & Elm, J. (2024). Reparameterization of GFN1-xTB for atmospheric molecular clusters: applications to multi-acid-multi-base systems. RSC Advances, 14(28), 20048-20055. https://doi.org/10.1039/d4ra03021d

Kubečka, J., Ayoubi, D., Tang, Z., Knattrup, Y., Engsvang, M., Wu, H. & Elm, J. (2024). Accurate modeling of the potential energy surface of atmospheric molecular clusters boosted by neural networks. Environmental science: Advances, 3(10), 1438-1451. https://doi.org/10.1039/d4va00255e

Nielsen, R. V., Salimi, M., Andersen, J. E. V., Elm, J., Dantan, A. & Pedersen, H. B. (2024). A new setup for measurements of absolute saturation vapor pressures using a dynamical method: Experimental concept and validation. Review of Scientific Instruments, 95(6), Article 065007. https://doi.org/10.1063/5.0215176

Pedersen, A. N., Knattrup, Y. & Elm, J. (2024). A cluster-of-functional-groups approach for studying organic enhanced atmospheric cluster formation. Aerosol Research, 2(1), 123-134. https://doi.org/10.5194/ar-2-123-2024

Zhang, R., Ma, F., Zhang, Y., Chen, J., Elm, J., He, X.-C. & Xie, H.-B. (2024). HIO₃-HIO₂-Driven Three-Component Nucleation: Screening Model and Cluster Formation Mechanism. Environmental Science & Technology, 58(1), 649-659. https://doi.org/10.1021/acs.est.3c06098

Ayoubi, D., Knattrup, Y. & Elm, J. (2023). Clusteromics V: Organic Enhanced Atmospheric Cluster Formation. ACS Omega, 8(10), 9621-9629. https://doi.org/10.1021/acsomega.3c00251

Cai, R., Yin, R., Li, X., Xie, H. B., Yang, D., Kerminen, V. M., Smith, J. N., Ma, Y., Hao, J., Chen, J., Kulmala, M., Zheng, J., Jiang, J. & Elm, J. (2023). Significant contributions of trimethylamine to sulfuric acid nucleation in polluted environments. npj Climate and Atmospheric Science, 6(1), Article 75. https://doi.org/10.1038/s41612-023-00405-3

Engsvang, M., Kubečka, J. & Elm, J. (2023). Toward Modeling the Growth of Large Atmospheric Sulfuric Acid-Ammonia Clusters. ACS Omega, 8(38), 34597-34609. https://doi.org/10.1021/acsomega.3c03521

Fomete, S. K. W., Kubečka, J., Elm, J. & Jen, C. N. (2023). Limited Role of Malonic Acid in Sulfuric Acid-Dimethylamine New Particle Formation. ACS Omega, 8(22), 19807-19815. https://doi.org/10.1021/acsomega.3c01643

Fu, Z., Ma, F., Liu, Y., Yan, C., Huang, D., Chen, J., Elm, J., Li, Y., Ding, A., Pichelstorfer, L., Xie, H. B., Nie, W., Francisco, J. S. & Zhou, P. (2023). An overlooked oxidation mechanism of toluene: computational predictions and experimental validations. Chemical Science, 14(45), 13050-13059. https://doi.org/10.1039/d3sc03638c

Knattrup, Y., Kubečka, J., Ayoubi, D. & Elm, J. (2023). Clusterome: A Comprehensive Data Set of Atmospheric Molecular Clusters for Machine Learning Applications. ACS Omega, 8(28), 25155-25164. https://doi.org/10.1021/acsomega.3c02203

Knattrup, Y., Kubečka, J. & Elm, J. (2023). Nitric Acid and Organic Acids Suppress the Role of Methanesulfonic Acid in Atmospheric New Particle Formation. The Journal of Physical Chemistry A, 127(36), 7568-7578. https://doi.org/10.1021/acs.jpca.3c04393

Kubečka, J., Neefjes, I., Besel, V., Qiao, F., Xie, H.-B. & Elm, J. (2023). Atmospheric Sulfuric Acid-Multi-Base New Particle Formation Revealed through Quantum Chemistry Enhanced by Machine Learning. The journal of physical chemistry. A, 127(9), 2091-2103. https://doi.org/10.1021/acs.jpca.3c00068

Kubečka, J., Besel, V., Neefjes, I., Knattrup, Y., Kurtén, T., Vehkamäki, H. & Elm, J. (2023). Computational Tools for Handling Molecular Clusters: Configurational Sampling, Storage, Analysis, and Machine Learning. ACS Omega, 8(47), 45115-45128. https://doi.org/10.1021/acsomega.3c07412

Kubečka, J., Knattrup, Y., Engsvang, M., Jensen, A. B., Ayoubi, D., Wu, H., Christiansen, O. & Elm, J. (2023). Current and future machine learning approaches for modeling atmospheric cluster formation. Nature Computational Science, 3(6), 495-503. https://doi.org/10.1038/s43588-023-00435-0

Ma, F., Xie, H.-B., Zhang, R., Su, L., Jiang, Q., Tang, W., Chen, J., Engsvang, M., Elm, J. & He, X.-C. (2023). Enhancement of Atmospheric Nucleation Precursors on Iodic Acid-Induced Nucleation: Predictive Model and Mechanism. Environmental Science & Technology, 57(17), 6944-6954. https://doi.org/10.1021/acs.est.3c01034

Olenius, T., Bergström, R., Kubečka, J., Myllys, N. & Elm, J. (2023). Reducing chemical complexity in representation of new-particle formation: evaluation of simplification approaches. Environmental Science: Atmospheres, 2023(3), 552-567. https://doi.org/10.1039/d2ea00174h

Wu, H., Engsvang, M., Knattrup, Y., Kubečka, J. & Elm, J. (2023). Improved Configurational Sampling Protocol for Large Atmospheric Molecular Clusters. ACS Omega, 8(47), 45065-45077. https://doi.org/10.1021/acsomega.3c06794

Zhao, Q., Xie, H. B., Ma, F., Nie, W., Yan, C., Huang, D., Elm, J. & Chen, J. (2023). Mechanism-based structure-activity relationship investigation on hydrolysis kinetics of atmospheric organic nitrates. npj Climate and Atmospheric Science, 6(1), Article 192. https://doi.org/10.1038/s41612-023-00517-w

Becker, D., Heitland, J., Carlsson, P. T. M., Elm, J., Olenius, T., Tödter, S., Kharrazizadeh, A. & Zeuch, T. (2022). Real-time monitoring of aerosol particle formation from sulfuric acid vapor at elevated concentrations and temperatures. Physical Chemistry Chemical Physics, 24(8), 5001-5013. https://doi.org/10.1039/d1cp04580f

Elm, J. (2022). Clusteromics III: Acid Synergy in Sulfuric Acid-Methanesulfonic Acid-Base Cluster Formation. ACS Omega, 7(17), 15206-15214. https://doi.org/10.1021/acsomega.2c01396

Engsvang, M. & Elm, J. (2022). Modeling the Binding Free Energy of Large Atmospheric Sulfuric Acid-Ammonia Clusters. ACS Omega, 7(9), 8077-8083. https://doi.org/10.1021/acsomega.1c07303

Fu, Z., Xie, H. B., Elm, J., Liu, Y., Fu, Z. & Chen, J. (2022). Atmospheric Autoxidation of Organophosphate Esters. Environmental Science & Technology, 56(11), 6944–6955. https://doi.org/10.1021/acs.est.1c04817

Jensen, A. B., Kubečka, J., Schmitz, G., Christiansen, O. & Elm, J. (2022). Massive Assessment of the Binding Energies of Atmospheric Molecular Clusters. Journal of Chemical Theory and Computation, 18(12), 7373-7383. https://doi.org/10.1021/acs.jctc.2c00825

Knattrup, Y. & Elm, J. (2022). Clusteromics IV: The Role of Nitric Acid in Atmospheric Cluster Formation. ACS Omega, 7(35), 31551–31560. https://doi.org/10.1021/acsomega.2c04278

Kubečka, J., Christensen, A. S., Rasmussen, F. R. & Elm, J. (2022). Quantum Machine Learning Approach for Studying Atmospheric Cluster Formation. Environmental Science and Technology Letters, 9(3), 239-244. https://doi.org/10.1021/acs.estlett.1c00997

Liu, Y., Xie, H.-B., Ma, F., Chen, J. & Elm, J. (2022). Amine-Enhanced Methanesulfonic Acid-Driven Nucleation: Predictive Model and Cluster Formation Mechanism. Environmental Science & Technology, 56(12), 7751-7760. https://doi.org/10.1021/acs.est.2c01639

Rasmussen, F. R., Kubecka, J. & Elm, J. (2022). Contribution of Methanesulfonic Acid to the Formation of Molecular Clusters in the Marine Atmosphere. The Journal of Physical Chemistry A, 126(40), 7127–7136. https://doi.org/10.1021/acs.jpca.2c04468

Rosati, B., Isokaanta, S., Christiansen, S., Jensen, M. M., Moosakutty, S. P., de Jonge, R. W., Massling, A., Glasius, M., Elm, J., Virtanen, A. & Bilde, M. (2022). Hygroscopicity and CCN potential of DMS-derived aerosol particles. Atmospheric Chemistry and Physics, 22(20), 13449-13466. https://doi.org/10.5194/acp-22-13449-2022

Thomsen, D., Thomsen, L. D., Iversen, E. M., Björgvinsdóttir, T. N., Vinther, S. F., Skønager, J. T., Hoffmann, T., Elm, J., Bilde, M. & Glasius, M. (2022). Ozonolysis of α-Pinene and Δ³-Carene Mixtures: Formation of Dimers with Two Precursors. Environmental Science & Technology, 56(23), 16643-16651. https://doi.org/10.1021/acs.est.2c04786

Xue, J., Ma, F., Elm, J., Chen, J. & Xie, H.-B. (2022). Atmospheric oxidation mechanism and kinetics of indole initiated by •OH and •Cl: a computational study. Atmospheric Chemistry and Physics, 22(17), 11543-11555. https://doi.org/10.5194/acp-22-11543-2022

Zhang, R., Shen, J., Xie, H. B., Chen, J. & Elm, J. (2022). The role of organic acids in new particle formation from methanesulfonic acid and methylamine. Atmospheric Chemistry and Physics, 22(4), 2639-2650. https://doi.org/10.5194/acp-22-2639-2022

Elm, J. (2021). Clusteromics I: Principles, Protocols, and Applications to Sulfuric Acid-Base Cluster Formation. ACS Omega, 6(11), 7804-7814. https://doi.org/10.1021/acsomega.1c00306

Elm, J. (2021). Clusteromics II: Methanesulfonic Acid-Base Cluster Formation. ACS Omega, 6(26), 17035-17044. https://doi.org/10.1021/acsomega.1c02115

Elm, J. (2021). Toward a Holistic Understanding of the Formation and Growth of Atmospheric Molecular Clusters: A Quantum Machine Learning Perspective. The Journal of Physical Chemistry A, 125(4), 895-902. https://doi.org/10.1021/acs.jpca.0c09762

Ma, F., Guo, X., Xia, D., Xie, H. B., Wang, Y., Elm, J., Chen, J. & Niu, J. (2021). Atmospheric Chemistry of Allylic Radicals from Isoprene: A Successive Cyclization-Driven Autoxidation Mechanism. Environmental Science & Technology, 55(8), 4399-4409. https://doi.org/10.1021/acs.est.0c07925

Rosati, B., Christiansen, S., Wollesen De Jonge, R., Roldin, P., Jensen, M. M., Wang, K., Moosakutty, S. P., Thomsen, D., Salomonsen, C., Hyttinen, N., Elm, J., Feilberg, A., Glasius, M. & Bilde, M. (2021). New Particle Formation and Growth from Dimethyl Sulfide Oxidation by Hydroxyl Radicals. ACS Earth and Space Chemistry, 5(4), 801-811. https://doi.org/10.1021/acsearthspacechem.0c00333

Thomsen, D., Elm, J., Rosati, B., Skønager, J. T., Bilde, M. & Glasius, M. (2021). Large Discrepancy in the Formation of Secondary Organic Aerosols from Structurally Similar Monoterpenes. ACS Earth and Space Chemistry, 5(3), 632-644. https://doi.org/10.1021/acsearthspacechem.0c00332

Wollesen De Jonge, R., Elm, J., Rosati, B., Christiansen, S., Hyttinen, N., Lüdemann, D., Bilde, M. & Roldin, P. (2021). Secondary aerosol formation from dimethyl sulfide-improved mechanistic understanding based on smog chamber experiments and modelling. Atmospheric Chemistry and Physics, 21(13), 9955-9976. https://doi.org/10.5194/acp-21-9955-2021

Xie, H. B. & Elm, J. (2021). Tri-base synergy in sulfuric acid-base clusters. Atmosphere, 12(10), Article 1260. https://doi.org/10.3390/atmos12101260

Carlsson, P. T. M., Celik, S., Becker, D., Olenius, T., Elm, J. & Zeuch, T. (2020). Neutral Sulfuric Acid-Water Clustering Rates: Bridging the Gap between Molecular Simulation and Experiment. The Journal of Physical Chemistry Letters, 11(10), 4239-4244. https://doi.org/10.1021/acs.jpclett.0c01045

Elm, J., Kubečka, J., Besel, V., Jääskeläinen, M. J., Halonen, R., Kurtén, T. & Vehkamäki, H. (2020). Modeling the formation and growth of atmospheric molecular clusters: A review. Journal of Aerosol Science, 149, Article 105621. https://doi.org/10.1016/j.jaerosci.2020.105621

Fu, Z., Xie, H. B., Elm, J., Guo, X., Fu, Z. & Chen, J. (2020). Formation of Low-Volatile Products and Unexpected High Formaldehyde Yield from the Atmospheric Oxidation of Methylsiloxanes. Environmental Science & Technology, 54(12), 7136-7145. https://doi.org/10.1021/acs.est.0c01090

Hyttinen, N., Heshmatnezhad, R., Elm, J., Kurtén, T. & Prisle, N. L. (2020). Technical note: Estimating aqueous solubilities and activity coefficients of mono- And α,ω-dicarboxylic acids using COSMOtherm. Atmospheric Chemistry and Physics, 20(21), 13131-13143. https://doi.org/10.5194/acp-20-13131-2020

Hyttinen, N., Elm, J., Malila, J., Calderón, S. M. & Prisle, N. L. (2020). Thermodynamic properties of isoprene-and monoterpene-derived organosulfates estimated with COSMOtherm. Atmospheric Chemistry and Physics, 20(9), 5679-5696. https://doi.org/10.5194/acp-20-5679-2020

Kristensen, K., N. Jensen, L., L. J. Quéléver, L., Christiansen, S., Rosati, B., Elm, J., Teiwes, R., B. Pedersen, H., Glasius, M., Ehn, M. & Bilde, M. (2020). The Aarhus Chamber Campaign on Highly Oxygenated Organic Molecules and Aerosols (ACCHA): Particle formation, organic acids, and dimer esters from α-pinene ozonolysis at different temperatures. Atmospheric Chemistry and Physics, 20(21), 12549-12567. https://doi.org/10.5194/acp-20-12549-2020

Pedersen, H. B., Elm, J., Frederiksen, C. H., Jessen, S. P. S., Teiwes, R. & Bilde, M. (2020). The reaction of isotope-substituted hydrated iodide I(H ¹⁸₂O)− with ozone: the reactive influence of the solvent water molecul. Physical Chemistry Chemical Physics, 22(34), 19080-19088. https://doi.org/10.1039/d0cp03219k

Rasmussen, F. R., Kubečka, J., Besel, V., Vehkamäki, H., Mikkelsen, K. V., Bilde, M. & Elm, J. (2020). Hydration of Atmospheric Molecular Clusters III: Procedure for Efficient Free Energy Surface Exploration of Large Hydrated Clusters. The journal of physical chemistry. A, 124(25), 5253-5261. https://doi.org/10.1021/acs.jpca.0c02932

Schmitz, G. & Elm, J. (2020). Assessment of the DLPNO Binding Energies of Strongly Noncovalent Bonded Atmospheric Molecular Clusters. ACS Omega, 5(13), 7601-7612. https://doi.org/10.1021/acsomega.0c00436

Shen, J., Elm, J., Xie, H. B., Chen, J., Niu, J. & Vehkamäki, H. (2020). Structural Effects of Amines in Enhancing Methanesulfonic Acid-Driven New Particle Formation. Environmental Science & Technology, 54(21), 13498-13508. https://doi.org/10.1021/acs.est.0c05358

Elm, J. (2019). An atmospheric cluster database consisting of sulfuric acid, bases, organics, and water. ACS Omega, 4(6), 10965-10974. https://doi.org/10.1021/acsomega.9b00860

Elm, J., Hyttinen, N., Lin, J. J., Kurtén, T. & Prisle, N. L. (2019). Strong Even/Odd Pattern in the Computed Gas-Phase Stability of Dicarboxylic Acid Dimers: Implications for Condensation Thermodynamics. The Journal of Physical Chemistry A, 123(44), 9594-9599. https://doi.org/10.1021/acs.jpca.9b08020

Elm, J. (2019). Unexpected Growth Coordinate in Large Clusters Consisting of Sulfuric Acid and C ₈ H ₁₂ O ₆ Tricarboxylic Acid. The Journal of Physical Chemistry A, 123(14), 3170-3175. https://doi.org/10.1021/acs.jpca.9b00428

Joranger, T., Kildgaard, J. V., Jorgensen, S., Elm, J. & Mikkelsen, K. V. (2019). Benchmarking sampling methodology for calculations of Rayleigh light scattering properties of atmospheric molecular clusters. Physical Chemistry Chemical Physics, 21(31), 17274-17287. https://doi.org/10.1039/c9cp02573a

Liu, C., Ma, F., Elm, J., Fu, Z., Tang, W., Chen, J. & Xie, H. B. (2019). Mechanism and predictive model development of reaction rate constants for N-center radicals with O₂. Chemosphere, 237, Article 124411. https://doi.org/10.1016/j.chemosphere.2019.124411

Ma, F., Xie, H.-B., Elm, J., Shen, J., Chen, J. & Vehkamaki, H. (2019). Piperazine enhancing sulfuric acid-based new particle formation: implications for the atmospheric fate of piperazine. Environmental Science & Technology, 53(15), 8785-8795. https://doi.org/10.1021/acs.est.9b02117

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Jonas Elm