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- Creator:
- Craven, Nicholas C, Singh, Ramanish, Quach, Co D, Gilmer, Justin B, Crawford, Brad, Marin-Rimoldi, Eliseo, Smith, Ryan, DeFever, Ryan, Dyukov, Maxim, Fothergill, Jenny, Jones, Chris, Moore, Timothy, Butler, Brandon L, Anderson, Joshua A, Iacovella, Christopher, Jankowski, Eric, Maginn, Eric, Potoff, Jeffrey, Glotzer, Sharon C, McCabe, Clare, Cummings, Peter T, and Siepmann, Ilja J
- Description:
- Data are collected in 5 separate workspace, one for the main density data calculations across the space and 4 for the subproject simulations that were performed to validate and dive deeper into specific engine implementations. In order to copy the simulation trajectory and calculated averages used to generate figures, these workspace folders must be downloaded and pointed to the correct place in the GitHub Project Structure, which can be found at https://github.com/mosdef-hub/reproducibility_study and Each compressed file contains the data for a single workspace.
- Keyword:
- molecular dynamics, monte carlo, reproducibility, and replicability
- Citation to related publication:
- https://doi.org/10.1021/acs.jced.5c00010
- Discipline:
- Science
-
- Creator:
- Raymond, Matt, Elvati, Paolo, Saldinger, Jacob C, Lin, Jonathan, Shi, Xuetao, and Violi, Angela
- Description:
- Nanoparticles (NPs) formed in nonthermal plasmas (NTPs) can have unique properties and applications. However, modeling their growth in these environments presents significant challenges due to the non-equilibrium nature of NTPs, making them computationally expensive to describe. In this work, we address the challenges associated with accelerating the estimation of parameters needed for these models. Specifically, we explore how different machine learning models can be tailored to improve prediction outcomes. We apply these methods to reactive classical molecular dynamics data, which capture the processes associated with colliding silane fragments in NTPs. These reactions exemplify processes where qualitative trends are clear, but their quantification is challenging, hard to generalize, and requires time-consuming simulations. Our results demonstrate that good prediction performance can be achieved when appropriate loss functions are implemented and correct invariances are imposed. While the diversity of molecules used in the training set is critical for accurate prediction, our findings indicate that only a fraction (15-25%) of the energy and temperature sampling is required to achieve high levels of accuracy. This suggests a substantial reduction in computational effort is possible for similar systems.
- Keyword:
- machine learning, molecular dynamics, nanoparticle, nonthermal plasma, silane, and sticking coefficient
- Citation to related publication:
- Raymond, M., Elvati, P., Saldinger, J. C., Lin, J., Shi, X., & Violi, A. (2025). Machine learning models for Si nanoparticle growth in nonthermal plasma. Plasma Sources Science and Technology. https://doi.org/10.1088/1361-6595/adbae1 and https://arxiv.org/abs/2501.00003
- Discipline:
- Science
-
- Creator:
- Luyet, Chloe, Elvati, Paolo, Vinh, Jordan, and Violi, Angela
- Description:
- A growing body of work has linked key biological activities to the mechanical properties of cellular membranes, and as a means of identification. Here, we present a computational approach to simulate and compare the vibrational spectra in the low-THz region for mammalian and bacterial membranes, investigating the effect of membrane asymmetry and composition, as well as the conserved frequencies of a specific cell. We find that asymmetry does not impact the vibrational spectra, and the impact of sterols depends on the mobility of the components of the membrane. We demonstrate that vibrational spectra can be used to distinguish between membranes and, therefore, could be used in identification of different organisms. The method presented, here, can be immediately extended to other biological structures (e.g., amyloid fibers, polysaccharides, and protein-ligand structures) in order to fingerprint and understand vibrations of numerous biologically-relevant nanoscale structures.
- Keyword:
- molecular dynamics, membranes, mechanical vibration, bacterial identification, and Staphylococcus aureus
- Citation to related publication:
- Luyet C, Elvati P, Vinh J, Violi A. Low-THz Vibrations of Biological Membranes. Membranes. 2023; 13(2):139. https://doi.org/10.3390/membranes13020139
- Discipline:
- Engineering