Submission information
Submission Number: 174
Submission ID: 3943
Submission UUID: 82bbfbee-77a2-47a9-b723-df62f67df1f3
Submission URI: /form/project
Created: Mon, 08/21/2023 - 06:17
Completed: Mon, 08/21/2023 - 06:17
Changed: Thu, 06/13/2024 - 06:29
Remote IP address: 146.75.253.174
Submitted by: Gaurav Khanna
Language: English
Is draft: No
Webform: Project
| Project Title | Developing machine learning interatomic potentials for classical molecular dynamics simulations of complex perovskites |
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| Program | CAREERS |
| Project Image |
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| Tags | machine-learning (272), molecular-dynamics (288) |
| Status | Complete |
| Project Leader | Ash Giri |
| ashgiri@uri.edu | |
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| Mentor(s) | Ashutosh Giri, Michael Strickler |
| Student-facilitator(s) | Jaymes Dionne |
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| Project Description | Our research group is developing machine learned (ML)-interatomic potentials for molecular dynamics simulations geared towards understanding the thermal properties of complex perovskites structures. The perovskite materials that will be modeled under this project will include metal halide perovskites and oxide-based perovskites. The ML-based potential development process will include gathering training data via density functional theory calculations followed by the utilization of deep learning framework to construct deep potential neural model. Ultimately, the potentials will be utilized for molecular dynamics simulations of the perovskites that will be performed with the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) package. We will utilize URI’s HPC or the UNITY cluster to perform the tasks. The student will obtain extensive experience working on an HPC cluster (command-line Linux, LAMMPS package, SLURM job scheduler, optimal submission parameters etc.) and will also learn to use the generated data-sets to train a ML/DL model. |
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| Student Research Computing Facilitator Profile | |
| Mentee Research Computing Profile | |
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| Project Institution | University of Rhode Island |
| Project Address | |
| Anchor Institution | CR-University of Rhode Island |
| Preferred Start Date | |
| Start as soon as possible. | No |
| Project Urgency | Already behind3Start date is flexible |
| Expected Project Duration (in months) | 6 |
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| What will the student learn? | |
| What will the mentee learn? | |
| What will the Cyberteam program learn from this project? | |
| HPC resources needed to complete this project? | |
| Notes | |
| What is the impact on the development of the principal discipline(s) of the project? | My project should have a fairly high impact in the field of nano-scale transport, as it is utilizing novel visualization techniques for heat transfer. |
| What is the impact on other disciplines? | My project may have a limited impact on other disciplines, at least for now, as it is very theoretical and physics-oriented. |
| Is there an impact physical resources that form infrastructure? | |
| Is there an impact on the development of human resources for research computing? | |
| Is there an impact on institutional resources that form infrastructure? | |
| Is there an impact on information resources that form infrastructure? | |
| Is there an impact on technology transfer? | |
| Is there an impact on society beyond science and technology? | There could be an impact on society as my project was focused on uncovering physics of complex structures currently being investigated for renewable energy sources. |
| Lessons Learned | I learned quite a bit about using clusters to submit large-scale arrayed jobs, and new Matlab techniques for video creation. |
| Overall results | We found very interesting behaviors for heat transfer in complex systems, including the visualization of some wave/particle effects that have previously only been theorized. |