#COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol

Abigail Dommer; Lorenzo Casalino; Fiona Kearns; Mia Rosenfeld; Nicholas Wauer; Surl-Hee Ahn; John Russo; Sofia Oliveira; Clare Morris; Anthony Bogetti; Anda Trifan; Alexander Brace; Terra Sztain; Austin Clyde; Heng Ma; Chakra Chennubhotla; Hyungro Lee; Matteo Turilli; Syma Khalid; Teresa Tamayo-Mendoza; Matthew Welborn; Anders Christensen; Daniel Ga Smith; Zhuoran Qiao; Sai K Sirumalla; Michael O'Connor; Frederick Manby; Anima Anandkumar; David Hardy; James Phillips; Abraham Stern; Josh Romero; David Clark; Mitchell Dorrell; Tom Maiden; Lei Huang; John McCalpin; Christopher Woods; Alan Gray; Matt Williams; Bryan Barker; Harinda Rajapaksha; Richard Pitts; Tom Gibbs; John Stone; Daniel M Zuckerman; Adrian J Mulholland; Thomas Miller; Shantenu Jha; Arvind Ramanathan; Lillian Chong; Rommie E Amaro

doi:10.1101/2021.11.12.468428

#COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol

Abigail Dommer, Lorenzo Casalino, Fiona Kearns, Mia Rosenfeld, Nicholas Wauer, Surl-Hee Ahn, John Russo, Sofia Oliveira, Clare Morris, Anthony Bogetti, Anda Trifan, Alexander Brace, Terra Sztain, Austin Clyde, Heng Ma, Chakra Chennubhotla, Hyungro Lee, Matteo Turilli, Syma Khalid, Teresa Tamayo-MendozaMatthew Welborn, Anders Christensen, Daniel Ga Smith, Zhuoran Qiao, Sai K Sirumalla, Michael O'Connor, Frederick Manby, Anima Anandkumar, David Hardy, James Phillips, Abraham Stern, Josh Romero, David Clark, Mitchell Dorrell, Tom Maiden, Lei Huang, John McCalpin, Christopher Woods, Alan Gray, Matt Williams, Bryan Barker, Harinda Rajapaksha, Richard Pitts, Tom Gibbs, John Stone, Daniel M Zuckerman, Adrian J Mulholland, Thomas Miller, Shantenu Jha, Arvind Ramanathan, Lillian Chong, Rommie E Amaro

Research output: Contribution to journal › Article (Academic Journal) › peer-review

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Abstract

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus obscure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.

Original language	English
Pages (from-to)	28-44
Number of pages	17
Journal	International Journal of High Performance Computing Applications
Volume	37
Issue number	1
Early online date	2 Oct 2022
DOIs	https://doi.org/10.1101/2021.11.12.468428 https://doi.org/10.1177/10943420221128233
Publication status	Published - 1 Jan 2023

Bibliographical note

Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by National Science Foundation (CHE- 1801971); National Science Foundation (MCB- 2032054); National Science Foundation (OAC-1818253); National Science Foundation (TG-CHE060063); U.S. Department of Energy (DE-AC02-06CH11357); U.S. Department of Energy (DE-AC05- 00OR22725); National Institutes of Health (P41-GM104601); National Institutes of Health (R01-GM132826).

Funding Information:
We thank Prof. Kim Prather for inspiring and informative discussions about aerosols and for her commitment to convey the airborne nature of SARS-CoV-2. We thank D. Veesler for sharing the Delta spike NTD coordinates in advance of publication. We thank B. Messer, D. Maxwell, and the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory supported by the DOE under Contract DE-AC05-00OR22725. We thank the Texas Advanced Computing Center Frontera team, especially D. Stanzione and T. Cockerill, and for compute time made available through a Director’s Discretionary Allocation (NSF OAC-1818253). We thank the Argonne Leadership Computing Facility supported by the DOE under DE-AC02-06CH11357. We thank the Pittsburgh Supercomputer Center for providing priority queues on Bridges-2 through the XSEDE allocation NSF TG-CHE060063. We thank N. Kern and J. Lee of the CHARMM-GUI support team for help converting topologies between NAMD and GROMACS. We thank J. Copperman, G. Simpson, D. Aristoff, and J. Leung for valuable discussions and support from NIH grant GM115805. NAMD and VMD are funded by NIH P41-GM104601. This work was supported by the NSF Center for Aerosol Impacts on Chemistry of the Environment (CAICE), National Science Foundation Center for Chemical Innovation (NSF CHE-1801971), as well as NIH GM132826, NSF RAPID MCB-2032054, an award from the RCSA Research Corp., a UC San Diego Moore’s Cancer Center 2020 SARS-CoV-2 seed grant, to R.E.A. This work was also supported by Oracle Cloud credits and related resources provided by the Oracle for Research program. AJM and ASFO receive funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (PREDACTED Advanced Grant, Grant agreement No.: 101021207). The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by National Science Foundation (CHE- 1801971); National Science Foundation (MCB- 2032054); National Science Foundation (OAC-1818253); National Science Foundation (TG-CHE060063); U.S. Department of Energy (DE-AC02-06CH11357); U.S. Department of Energy (DE-AC05- 00OR22725); National Institutes of Health (P41-GM104601); National Institutes of Health (R01-GM132826).

Funding Information:
We thank Prof. Kim Prather for inspiring and informative discussions about aerosols and for her commitment to convey the airborne nature of SARS-CoV-2. We thank D. Veesler for sharing the Delta spike NTD coordinates in advance of publication. We thank B. Messer, D. Maxwell, and the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory supported by the DOE under Contract DE-AC05-00OR22725. We thank the Texas Advanced Computing Center Frontera team, especially D. Stanzione and T. Cockerill, and for compute time made available through a Director’s Discretionary Allocation (NSF OAC-1818253). We thank the Argonne Leadership Computing Facility supported by the DOE under DE-AC02-06CH11357. We thank the Pittsburgh Supercomputer Center for providing priority queues on Bridges-2 through the XSEDE allocation NSF TG-CHE060063. We thank N. Kern and J. Lee of the CHARMM-GUI support team for help converting topologies between NAMD and GROMACS. We thank J. Copperman, G. Simpson, D. Aristoff, and J. Leung for valuable discussions and support from NIH grant GM115805. NAMD and VMD are funded by NIH P41-GM104601. This work was supported by the NSF Center for Aerosol Impacts on Chemistry of the Environment (CAICE), National Science Foundation Center for Chemical Innovation (NSF CHE-1801971), as well as NIH GM132826, NSF RAPID MCB-2032054, an award from the RCSA Research Corp., a UC San Diego Moore’s Cancer Center 2020 SARS-CoV-2 seed grant, to R.E.A. This work was also supported by Oracle Cloud credits and related resources provided by the Oracle for Research program. AJM and ASFO receive funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (PREDACTED Advanced Grant, Grant agreement No.: 101021207).

Publisher Copyright:
© The Author(s) 2022.

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Dommer, A., Casalino, L., Kearns, F., Rosenfeld, M., Wauer, N., Ahn, S.-H., Russo, J., Oliveira, S., Morris, C., Bogetti, A., Trifan, A., Brace, A., Sztain, T., Clyde, A., Ma, H., Chennubhotla, C., Lee, H., Turilli, M., Khalid, S., ... Amaro, R. E. (2023). #COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol. International Journal of High Performance Computing Applications, 37(1), 28-44. https://doi.org/10.1101/2021.11.12.468428, https://doi.org/10.1177/10943420221128233

@article{f3704d7d5a454e8391b1ee278403bb12,

title = "#COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol",

abstract = "We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus obscure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.",

author = "Abigail Dommer and Lorenzo Casalino and Fiona Kearns and Mia Rosenfeld and Nicholas Wauer and Surl-Hee Ahn and John Russo and Sofia Oliveira and Clare Morris and Anthony Bogetti and Anda Trifan and Alexander Brace and Terra Sztain and Austin Clyde and Heng Ma and Chakra Chennubhotla and Hyungro Lee and Matteo Turilli and Syma Khalid and Teresa Tamayo-Mendoza and Matthew Welborn and Anders Christensen and Smith, {Daniel Ga} and Zhuoran Qiao and Sirumalla, {Sai K} and Michael O'Connor and Frederick Manby and Anima Anandkumar and David Hardy and James Phillips and Abraham Stern and Josh Romero and David Clark and Mitchell Dorrell and Tom Maiden and Lei Huang and John McCalpin and Christopher Woods and Alan Gray and Matt Williams and Bryan Barker and Harinda Rajapaksha and Richard Pitts and Tom Gibbs and John Stone and Zuckerman, {Daniel M} and Mulholland, {Adrian J} and Thomas Miller and Shantenu Jha and Arvind Ramanathan and Lillian Chong and Amaro, {Rommie E}",

note = "Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by National Science Foundation (CHE- 1801971); National Science Foundation (MCB- 2032054); National Science Foundation (OAC-1818253); National Science Foundation (TG-CHE060063); U.S. Department of Energy (DE-AC02-06CH11357); U.S. Department of Energy (DE-AC05- 00OR22725); National Institutes of Health (P41-GM104601); National Institutes of Health (R01-GM132826). Funding Information: We thank Prof. Kim Prather for inspiring and informative discussions about aerosols and for her commitment to convey the airborne nature of SARS-CoV-2. We thank D. Veesler for sharing the Delta spike NTD coordinates in advance of publication. We thank B. Messer, D. Maxwell, and the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory supported by the DOE under Contract DE-AC05-00OR22725. We thank the Texas Advanced Computing Center Frontera team, especially D. Stanzione and T. Cockerill, and for compute time made available through a Director{\textquoteright}s Discretionary Allocation (NSF OAC-1818253). We thank the Argonne Leadership Computing Facility supported by the DOE under DE-AC02-06CH11357. We thank the Pittsburgh Supercomputer Center for providing priority queues on Bridges-2 through the XSEDE allocation NSF TG-CHE060063. We thank N. Kern and J. Lee of the CHARMM-GUI support team for help converting topologies between NAMD and GROMACS. We thank J. Copperman, G. Simpson, D. Aristoff, and J. Leung for valuable discussions and support from NIH grant GM115805. NAMD and VMD are funded by NIH P41-GM104601. This work was supported by the NSF Center for Aerosol Impacts on Chemistry of the Environment (CAICE), National Science Foundation Center for Chemical Innovation (NSF CHE-1801971), as well as NIH GM132826, NSF RAPID MCB-2032054, an award from the RCSA Research Corp., a UC San Diego Moore{\textquoteright}s Cancer Center 2020 SARS-CoV-2 seed grant, to R.E.A. This work was also supported by Oracle Cloud credits and related resources provided by the Oracle for Research program. AJM and ASFO receive funding from the European Research Council (ERC) under the European Union{\textquoteright}s Horizon 2020 research and innovation programme (PREDACTED Advanced Grant, Grant agreement No.: 101021207). The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by National Science Foundation (CHE- 1801971); National Science Foundation (MCB- 2032054); National Science Foundation (OAC-1818253); National Science Foundation (TG-CHE060063); U.S. Department of Energy (DE-AC02-06CH11357); U.S. Department of Energy (DE-AC05- 00OR22725); National Institutes of Health (P41-GM104601); National Institutes of Health (R01-GM132826). Funding Information: We thank Prof. Kim Prather for inspiring and informative discussions about aerosols and for her commitment to convey the airborne nature of SARS-CoV-2. We thank D. Veesler for sharing the Delta spike NTD coordinates in advance of publication. We thank B. Messer, D. Maxwell, and the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory supported by the DOE under Contract DE-AC05-00OR22725. We thank the Texas Advanced Computing Center Frontera team, especially D. Stanzione and T. Cockerill, and for compute time made available through a Director{\textquoteright}s Discretionary Allocation (NSF OAC-1818253). We thank the Argonne Leadership Computing Facility supported by the DOE under DE-AC02-06CH11357. We thank the Pittsburgh Supercomputer Center for providing priority queues on Bridges-2 through the XSEDE allocation NSF TG-CHE060063. We thank N. Kern and J. Lee of the CHARMM-GUI support team for help converting topologies between NAMD and GROMACS. We thank J. Copperman, G. Simpson, D. Aristoff, and J. Leung for valuable discussions and support from NIH grant GM115805. NAMD and VMD are funded by NIH P41-GM104601. This work was supported by the NSF Center for Aerosol Impacts on Chemistry of the Environment (CAICE), National Science Foundation Center for Chemical Innovation (NSF CHE-1801971), as well as NIH GM132826, NSF RAPID MCB-2032054, an award from the RCSA Research Corp., a UC San Diego Moore{\textquoteright}s Cancer Center 2020 SARS-CoV-2 seed grant, to R.E.A. This work was also supported by Oracle Cloud credits and related resources provided by the Oracle for Research program. AJM and ASFO receive funding from the European Research Council (ERC) under the European Union{\textquoteright}s Horizon 2020 research and innovation programme (PREDACTED Advanced Grant, Grant agreement No.: 101021207). Publisher Copyright: {\textcopyright} The Author(s) 2022.",

year = "2023",

month = jan,

day = "1",

doi = "10.1101/2021.11.12.468428",

language = "English",

volume = "37",

pages = "28--44",

journal = "International Journal of High Performance Computing Applications",

issn = "1094-3420",

publisher = "SAGE Publications Inc.",

number = "1",

}

Dommer, A, Casalino, L, Kearns, F, Rosenfeld, M, Wauer, N, Ahn, S-H, Russo, J, Oliveira, S, Morris, C, Bogetti, A, Trifan, A, Brace, A, Sztain, T, Clyde, A, Ma, H, Chennubhotla, C, Lee, H, Turilli, M, Khalid, S, Tamayo-Mendoza, T, Welborn, M, Christensen, A, Smith, DG, Qiao, Z, Sirumalla, SK, O'Connor, M, Manby, F, Anandkumar, A, Hardy, D, Phillips, J, Stern, A, Romero, J, Clark, D, Dorrell, M, Maiden, T, Huang, L, McCalpin, J, Woods, C, Gray, A, Williams, M, Barker, B, Rajapaksha, H, Pitts, R, Gibbs, T, Stone, J, Zuckerman, DM, Mulholland, AJ, Miller, T, Jha, S, Ramanathan, A, Chong, L & Amaro, RE 2023, '#COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol', International Journal of High Performance Computing Applications, vol. 37, no. 1, pp. 28-44. https://doi.org/10.1101/2021.11.12.468428, https://doi.org/10.1177/10943420221128233

TY - JOUR

T1 - #COVIDisAirborne

T2 - AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol

AU - Dommer, Abigail

AU - Casalino, Lorenzo

AU - Kearns, Fiona

AU - Rosenfeld, Mia

AU - Wauer, Nicholas

AU - Ahn, Surl-Hee

AU - Russo, John

AU - Oliveira, Sofia

AU - Morris, Clare

AU - Bogetti, Anthony

AU - Trifan, Anda

AU - Brace, Alexander

AU - Sztain, Terra

AU - Clyde, Austin

AU - Ma, Heng

AU - Chennubhotla, Chakra

AU - Lee, Hyungro

AU - Turilli, Matteo

AU - Khalid, Syma

AU - Tamayo-Mendoza, Teresa

AU - Welborn, Matthew

AU - Christensen, Anders

AU - Smith, Daniel Ga

AU - Qiao, Zhuoran

AU - Sirumalla, Sai K

AU - O'Connor, Michael

AU - Manby, Frederick

AU - Anandkumar, Anima

AU - Hardy, David

AU - Phillips, James

AU - Stern, Abraham

AU - Romero, Josh

AU - Clark, David

AU - Dorrell, Mitchell

AU - Maiden, Tom

AU - Huang, Lei

AU - McCalpin, John

AU - Woods, Christopher

AU - Gray, Alan

AU - Williams, Matt

AU - Barker, Bryan

AU - Rajapaksha, Harinda

AU - Pitts, Richard

AU - Gibbs, Tom

AU - Stone, John

AU - Zuckerman, Daniel M

AU - Mulholland, Adrian J

AU - Miller, Thomas

AU - Jha, Shantenu

AU - Ramanathan, Arvind

AU - Chong, Lillian

AU - Amaro, Rommie E

N1 - Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by National Science Foundation (CHE- 1801971); National Science Foundation (MCB- 2032054); National Science Foundation (OAC-1818253); National Science Foundation (TG-CHE060063); U.S. Department of Energy (DE-AC02-06CH11357); U.S. Department of Energy (DE-AC05- 00OR22725); National Institutes of Health (P41-GM104601); National Institutes of Health (R01-GM132826). Funding Information: We thank Prof. Kim Prather for inspiring and informative discussions about aerosols and for her commitment to convey the airborne nature of SARS-CoV-2. We thank D. Veesler for sharing the Delta spike NTD coordinates in advance of publication. We thank B. Messer, D. Maxwell, and the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory supported by the DOE under Contract DE-AC05-00OR22725. We thank the Texas Advanced Computing Center Frontera team, especially D. Stanzione and T. Cockerill, and for compute time made available through a Director’s Discretionary Allocation (NSF OAC-1818253). We thank the Argonne Leadership Computing Facility supported by the DOE under DE-AC02-06CH11357. We thank the Pittsburgh Supercomputer Center for providing priority queues on Bridges-2 through the XSEDE allocation NSF TG-CHE060063. We thank N. Kern and J. Lee of the CHARMM-GUI support team for help converting topologies between NAMD and GROMACS. We thank J. Copperman, G. Simpson, D. Aristoff, and J. Leung for valuable discussions and support from NIH grant GM115805. NAMD and VMD are funded by NIH P41-GM104601. This work was supported by the NSF Center for Aerosol Impacts on Chemistry of the Environment (CAICE), National Science Foundation Center for Chemical Innovation (NSF CHE-1801971), as well as NIH GM132826, NSF RAPID MCB-2032054, an award from the RCSA Research Corp., a UC San Diego Moore’s Cancer Center 2020 SARS-CoV-2 seed grant, to R.E.A. This work was also supported by Oracle Cloud credits and related resources provided by the Oracle for Research program. AJM and ASFO receive funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (PREDACTED Advanced Grant, Grant agreement No.: 101021207). The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by National Science Foundation (CHE- 1801971); National Science Foundation (MCB- 2032054); National Science Foundation (OAC-1818253); National Science Foundation (TG-CHE060063); U.S. Department of Energy (DE-AC02-06CH11357); U.S. Department of Energy (DE-AC05- 00OR22725); National Institutes of Health (P41-GM104601); National Institutes of Health (R01-GM132826). Funding Information: We thank Prof. Kim Prather for inspiring and informative discussions about aerosols and for her commitment to convey the airborne nature of SARS-CoV-2. We thank D. Veesler for sharing the Delta spike NTD coordinates in advance of publication. We thank B. Messer, D. Maxwell, and the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory supported by the DOE under Contract DE-AC05-00OR22725. We thank the Texas Advanced Computing Center Frontera team, especially D. Stanzione and T. Cockerill, and for compute time made available through a Director’s Discretionary Allocation (NSF OAC-1818253). We thank the Argonne Leadership Computing Facility supported by the DOE under DE-AC02-06CH11357. We thank the Pittsburgh Supercomputer Center for providing priority queues on Bridges-2 through the XSEDE allocation NSF TG-CHE060063. We thank N. Kern and J. Lee of the CHARMM-GUI support team for help converting topologies between NAMD and GROMACS. We thank J. Copperman, G. Simpson, D. Aristoff, and J. Leung for valuable discussions and support from NIH grant GM115805. NAMD and VMD are funded by NIH P41-GM104601. This work was supported by the NSF Center for Aerosol Impacts on Chemistry of the Environment (CAICE), National Science Foundation Center for Chemical Innovation (NSF CHE-1801971), as well as NIH GM132826, NSF RAPID MCB-2032054, an award from the RCSA Research Corp., a UC San Diego Moore’s Cancer Center 2020 SARS-CoV-2 seed grant, to R.E.A. This work was also supported by Oracle Cloud credits and related resources provided by the Oracle for Research program. AJM and ASFO receive funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (PREDACTED Advanced Grant, Grant agreement No.: 101021207). Publisher Copyright: © The Author(s) 2022.

PY - 2023/1/1

Y1 - 2023/1/1

N2 - We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus obscure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.

AB - We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus obscure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.

U2 - 10.1101/2021.11.12.468428

DO - 10.1101/2021.11.12.468428

M3 - Article (Academic Journal)

C2 - 34816263

SN - 1094-3420

VL - 37

SP - 28

EP - 44

JO - International Journal of High Performance Computing Applications

JF - International Journal of High Performance Computing Applications

IS - 1

ER -

#COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol

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