Name | Region | Skills | Interests |
---|---|---|---|
Anita Orendt | Campus Champions, RMACC | ||
Alexander Pacheco | |||
Balamurugan Desinghu | ACCESS CSSN, Campus Champions, CAREERS, Northeast | ||
Edwin Posada | Campus Champions | ||
Yu-Chieh Chi | Campus Champions | ||
Jacob Fosso Tande | Campus Champions | ||
Jordan Hayes | Campus Champions | ||
Jonathan Lyon | At-Large, Campus Champions, Kentucky, ACCESS CSSN | ||
Lisa Perez | SWEETER | ||
Jeffrey J. Nuc… | CAREERS | ||
Justin Oelgoetz | Campus Champions | ||
Russell Hofmann | ACCESS CSSN | ||
Xiaoqin Huang | ACCESS CSSN | ||
Swabir Silayi | Campus Champions |
Name | Roles | Skills | Interests |
---|---|---|---|
Jonathan Lyon |
mentor researcher/educator |
Project Title | Project Institution | Project Owner | Tags | Status |
---|---|---|---|---|
USD CI Improvements to Advanced Chemistry and Neuroscience Research | University of South Dakota | Kevin Brandt | administering-hpc, computational-chemistry, file-transfer, gaussian, github, globus, research-facilitation, research-grants, training, workflow | Finishing Up |
Title | Category | Tags | Skill Level |
---|---|---|---|
Gaussian 16 | Tool | gaussian, computational-chemistry | 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.
Out of all the upper level chemistry courses, physical chemistry is the only course that provides an in-depth insight into the fundamental principles underpinning the concepts taught in various sub-disciplines of chemistry. Further, physical chemistry provides a connection between microscopic and macroscopic worlds of chemistry through mathematical models and experimental methods to test the validity of those models. Therefore, computational techniques are a perfect vehicle to teach content of physical chemistry course to undergraduate students. Additionally, American Chemical Society recommends computational chemistry to be incorporated into undergraduate chemistry curriculum. At Bridgewater State University (BSU) physical chemistry is a two-semester course referred to as 'physical chemistry I' and 'physical chemistry II'. While the overarching goal is to develop computational experiments (referred to as 'dry-labs'), project proposed here focuses on designing and developing dry labs for 'Physical Chemistry II' course at BSU. The inherently theoretical nature of this course along with its connection to wide range of spectroscopic techniques commonly used by chemists and physicists makes this course a perfect choice for assessing BSU students' reception to the idea of dry labs. It should be noted that there are no computational experiments in the current physical chemistry curriculum (both I and II) at BSU. The proposed project focuses on developing 4 - 6 computational experiments to be introduced (in spring 2018) as either stand-alone dry-lab experiments or accompany currently existing experiments. These dry labs will be developed on Gaussian 09 platform, which is currently installed on C3DDB server at MGHPCC. Finally, I also expect to make these experiments available to other New England instructors teaching physical chemistry II or equivalent course interested in incorporating computational chemistry into their curriculum.
Rutgers, the State University of New Jersey
ACCESS CSSN, Campus Champions, CAREERS, Northeast
mentor, researcher/educator, research computing facilitator, cssn, Consultant
University of Utah
Campus Champions, RMACC
mentor, regional facilitator, research computing facilitator
Rutgers, the State University of New Jersey
ACCESS CSSN, Campus Champions, CAREERS, Northeast
mentor, researcher/educator, research computing facilitator, cssn, Consultant
Old Dominion University
Campus Champions, Northeast, ACCESS CSSN
mentor, research computing facilitator, cssn
University of Missouri-Kansas City
Campus Champions
researcher/educator, research computing facilitator