NCSI: Simulation of Proposed Bio-based Asphalt Replacement Materials using Molecular Dynamics

Status	Completed
Mentor Name	Michael Greenfield
Mentor's XSEDE Affiliation	Faculty member new to XSEDE with long-time HPC experience, fall 2021 EMPOWER mentor
Mentor Has Been in XSEDE Community	Less than 1 year
Project Title	Simulation of Proposed Bio-based Asphalt Replacement Materials using Molecular Dynamics
Summary	Collaborators have devised (experimentally) a sustainable bio-sourced alternative material that can reduce needs in pavement for petroleum-based asphalt. We have estimated model compositions (via Reverse Monte Carlo computations) that can represent this bio-based multicomponent system in molecular simulations. In the project, conducting and interpreting fully atomistic molecular dynamics simulations will continue in order to infer how the presence and activity of different molecule types impacts the predicted mechanical properties.
Job Description	The student will run the molecular dynamics simulations. Then they will compute time autocorrelation functions of stress, molecule rotation, and diffusion, which they will analyze in order to quantify the temperature- and frequency-dependent mechanical properties of the model system. They will compare the results to whatever experimental data are available. The results will be included in a journal publication, with the student as a co-author.
Computational Resources	The LAMMPS package of Sandia National Labs will be the software employed for molecular dynamics simulations run in parallel. Software developed within the group will be used to compute and analyze the correlation functions, including application of noise reduction tools as required. Linux clusters available at the university will be used for parallel computations initially, and XSEDE resources will be sought for future extensions as the project needs grow to exceed campus resources.
Contribution to Community	The overall project will demonstrate ways that computation can complement more typical experimental approaches in materials development. Prior simulations by this research group catalyzed significant growth in applications of molecular dynamics simulations toward analyzing the properties of model asphalt systems. This project will enable analogous future efforts to be conducted on a bio-sourced asphalt replacement material. In the long term, it is desirable to replace petroleum-based products with bio-sourced sustainable alternatives. This project demonstrates how HPC and members of the greater computationally-aware engineering community, including XSEDE, can play a role in such new developments.
Position Type	Apprentice
Training Plan	The proposed student candidate is an EMPOWER apprentice in fall 2021. This is after they spent prior semesters starting to learn methods of molecular dynamics and running preliminary simulations. Currently they are running molecular dynamics simulations in a very guided environment and are learning how to assess when a simulation has equilibrated. They are starting to recognize when results show initial indications of convergence, when simulation results can be trusted, and when the results should be questioned. In the spring 2022 apprentice period, they will run the simulations with a greater degree of independence and will take more ownership of the process as they progress as an apprentice. Through hands-on analysis of their own MD results, they will be exposed to how analysis of correlation functions plays an important role in these considerations. They will apply their current skills by running additional simulations with different initial random number seeds, which will increate statistical significance as they compare their diverse results. Finally, they will simulate multiple temperatures and compositions to quantify how changes in conditions affect the predicted mechanical properties.
Student Prerequisites/Conditions/Qualifications	Prior initial experience with running molecular dynamics simulations. Strong quantitative skills and attention to detail. Programming experience in a computer language.
Duration	Semester
Start Date	01/18/2022
End Date	04/30/2022