Name | Region | Skills | Interests |
---|---|---|---|
Alexander Pacheco | |||
Ben Lynch | Campus Champions | ||
Christopher Bl… | Campus Champions | ||
Bala Desinghu | ACCESS CSSN, Campus Champions, CAREERS, Northeast | ||
diana Trotman | CAREERS | ||
Deborah Penchoff | Campus Champions | ||
Edwin Posada | Campus Champions | ||
Gil Speyer | ACCESS CSSN, RMACC, Campus Champions | ||
Yu-Chieh Chi | Campus Champions | ||
Jacob Fosso Tande | Campus Champions | ||
Jonathan Lyon | At-Large, Campus Champions, Kentucky, ACCESS CSSN | ||
Jason Key | Campus Champions | ||
Lonnie Crosby | Campus Champions, ACCESS CSSN | ||
Marcus Bond | Great Plains, Campus Champions, CCMNet | ||
Lisa Perez | SWEETER | ||
Nannan Shan | CCMNet, ACCESS CSSN | ||
Justin Oelgoetz | Campus Champions, CCMNet | ||
Mark Perri | Campus Champions | ||
Paul Rulis | Campus Champions | ||
Russell Hofmann | ACCESS CSSN, CCMNet | ||
Sean Anderson | Campus Champions, CCMNet | ||
Xiaoqin Huang | ACCESS CSSN | ||
Swabir Silayi | ACCESS CSSN, CCMNet, Campus Champions | ||
Spencer Pruitt | Northeast | ||
Torey Battelle | Campus Champions, CCMNet | ||
Thomas Cheatham | Campus Champions, RMACC |
Title | Date |
---|---|
Ookami Webinar | 02/14/24 |
Open Call: Minisymposia for PASC24 | 10/05/23 |
Title | Category | Tags | Skill Level |
---|---|---|---|
Awesome Jupyter Widgets (for building interactive scientific workflows or science gateway tools) | Learning | ai, computer-graphics, plotting, visualization, big-data, data-analysis, deep-learning, image-processing, machine-learning, monte-carlo, neural-networks, data-sharing, data-lifecycle, data-management, data-management-software, data-reproducibility, github, astrophysics, data-science, novel-accelerators, computational-chemistry, genomics, materials-science, gravitational-waves, oceanography, particle-physics, physiology, psychology, quantum-computing, quantum-mechanics, biology, open-ondemand, science-gateway, c++, jupyterhub, python | Beginner, Intermediate, Advanced |
CHARMM Links to Install, Run, and Troubleshoot MD Simulations | Learning | charmm, molecular-dynamics, namd, computational-chemistry | Beginner, Intermediate |
DeepChem | Tool | pytorch, tensorflow, computational-chemistry | Beginner, Intermediate, Advanced |
Solid metal hydrides are an attractive candidates for hydrogen storage materials. Magnesium has the benefit of being inexpensive, abundant, and non-toxic. However, the application of magnesium hydrides is limited by the hydrogen sorption kinetics. Doping magnesium hydrides with transition metal atoms improves this downfall, but much is still unknown about the process or the best choice of dopant type and concentration.
In this position, the student will study magnesium hydride clusters doped with early transition metals (e.g., Ti and V) as model systems for real world hydrogen storage materials. Specifically, we will search each cluster's potential energy surface for local and global minima and explore the relationship of cluster size and dopant concentration on different properties. The results from this investigation will then be compared with related cluster systems.
The student will begin by performing a literature search for this system, which will allow the student to pick an appropriate level of theory to conduct this investigation. This level will be chosen by performing calculations on the MgM, MgH, and MH (M = Ti and V) diatomic species (and select other sizes based on the results of the literature search) and comparing the predictions with experimentally determined spectroscopic data (e.g., bond length, stretching frequency, etc.). The student will then perform theoretical chemistry calculations using the Gaussian 16 and NBO 7 programs on the EXPANSE cluster housed at the San Diego Supercomputing Center (SDSC) through ACCESS allocation CHE-130094. First, this student will generate candidate structures for each cluster size and composition using two global optimization procedures. One program utilizes the artificial bee colony algorithm, whereas the second basin hoping program is written and compiled in-house using Fortran code. Additional structures will be generated by hand from our prior knowledge. All candidate structures will then be further optimized by the student at the appropriate level determined at the start of the semester. Higher level (e.g., double hybrid density functional theory) calculations will also be performed as further confirmation of the predicted results. Various results will be visualized with the Avogadro, Gabedit, and Gaussview programs on local machines.
George Mason University
ACCESS CSSN, CCMNet, Campus Champions
mentor, research computing facilitator, cssn, Consultant, CCMNet
Austin Peay State University
Campus Champions, CCMNet
mentor, researcher/educator, research computing facilitator, CCMNet