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In The international journal of high performance computing applications

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.

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


AI, COVID-19, Delta, GPU, HPC, SARS-CoV-2, aerosols, computational virology, deep learning, molecular dynamics, multiscale simulation, weighted ensemble