Instructor: A. J. Medford
TAs: Logan Brabson, Lucas Timmerman, Neung-Kyung Yu, Kaylee Tian, Sayan Bhowmik
Email: ajm@gatech.edu
Dr. Medford Office Hours: By Appointment
TA Office Hours: Set up via email or slack correspondence
Class Hours: W 11:00-11:50 am
Class Room: Zoom (see Canvas App)
Training Materials: Jupyter Book
Course Description#
About VIP#
The Vertically-Integrated Projects (VIP) Program operates in a research and development context. Undergraduate students that join VIP teams earn academic credit for their participation in design/discovery efforts that assist faculty and graduate students with research and development issues in their areas of expertise.
The teams are:
Multidisciplinary - drawing students from all disciplines on campus;
Vertically-integrated - maintaining a mix of sophomores through PhD students each semester;
Long-term - each undergraduate student may participate in a project for up to three years and each graduate student may participate for the duration of their graduate career.
The continuity, technical depth, and disciplinary breadth of these teams are intended to: Provide the time and context necessary for students to learn and practice many different professional skills, make substantial technical contributions to the project, and experience many different roles on a large, multidisciplinary design/discovery team. Support long-term interaction between the graduate and undergraduate students on the team. The graduate students mentor the undergraduates as they work on the design/discovery projects embedded in the graduate students’ research. Enable the completion of large-scale design/discovery projects that are of significant benefit to faculty members’ research programs.
About Big Data and Quantum Mechanics#
This course explores projects at the intersection of computational chemistry (quantum mechanics) and data science (big data) within the application domain of surface science and catalysis. The team merges expertise from computational and physical sciences, and students from computer science, electrical engineering, industrial & systems engineering, chemical engineering, chemistry, physics, and materials science. The VIP course consists of sub-teams with research topics developed by graduate students in the Medford group. More details of subteams are available in the [overview lecture].(https://medford-group.github.io/training-materials/docs/VIP_Overview.html)
Course Logistics#
The course will utilize the following resources for communication and submission of assignments:
Github: All course materials will be posted to Github.
Canvas: The course Canvas site will be used for submission of assignments and peer grading.
Slack: The group Slack channel is the preferred method of communication with instructors and graduate students. To join the channel login to the GT Slack enterprise account, then search for the “Big Data & Quantum Mechanics (VIP)” workspace and ask to join.
Course Objectives#
Generate atomistic simulation and adsorption energy data using density functional theory (DFT)
Converge numerical calculations with respect to input parameters
Submit, manage, and analyze high-performance computing jobs
Utilize machine-learning packages to model and predict the output of atomistic simulations
Work with a team to solve real-world problems at the intersection of big data and quantum mechanical simulations
Course Structure#
The grade will be assigned based on three categories:
Documentation: 33.3 %
Personal Accomplishments: 33.4 %
Teamwork and Interactions: 33.3 %
A grade of 0-5 will be assigned for each category based on the criteria outlined below. A total grade will be computed based on the weighted average of the 3 categories which will be converted to letter grades using the following:
A: > 4
B: > 3
C: < 3
D: < 2
You will receive a grade at the midterm and after the end of the course. Both grades will averaged to determine your final letter grade.
Documentation#
The documentation grade will be assessed based on bi-weekly updates submitted via Canvas. The weekly update should include the following components:
Tasks completed in the prior week
Discussion of any key challenges or results
Tasks to be completed in future weeks
Documentation needed to repeat analysis and access results
A literature review of at least one paper that you read or used during the weeks (see literature review lecture)
Documentation for the bi-weekly updates should be provided as a text file or Word document. Bi-weekly updates will be peer graded on a scale of 0-5 based on the following criteria (decimal scores are allowed):
5: Remarkable progress on tasks, clear description of process and findings, a well-defined plan for the next weeks, and relevant citations (A+)
4: Average progress on tasks, some documentation of results/challenges, a reasonable plan for the coming weeks, and relevant citation (A) Goals are completely achieved (A)
3: Some progress on tasks, but documentation and/or plan is lacking detail. A citation is provided, but lacks relevance or appears to be re-used (B)
2: Very little progress on tasks, documentation and/or plan is omitted or incomplete. Citation lacks relevance or is omitted (C)
1: Clear lack of effort in completing tasks, documentation and plan are omitted or irrelevant. Citation not provided (D)
0: No submission
Bi-weekly updates should be compiled into a single document and submitted at the midterm and final. Instructors will review the average score from bi-weekly updates and compare this to the compiled updates to ensure that the peer grading is appropriate, and to ensure that citations are not recycled. Instructors reserve the right to revise the average score up or down if substantial inconsistencies between the score and the criteria above are observed.
Personal Accomplishments#
Personal accomplishments will be measured by self-defined goals and will be assessed by each student’s respective sub-team mentor. Each goal should have a deliverable that can be unambiguously evaluated as complete or incomplete (computer code, report, figure, etc.), and each student should submit a deliverable. Deliverables submitted by team members need not be unique, but self-defined goals should be individualized, such that each team member has different deliverables that support a larger team-based goal.
5: Goals are completely achieved and additional progress has been made (A+)
4: Goals are completely achieved (A)
3: Goals are partially achieved (B)
2: Some progress has been made, but goals are not achieved (C)
1: No substantial progress is made towards goals (D)
The letter grade will be determined by the sub-team leader, and is generally controlled by two factors: i) ability to plan research and set realistic targets and ii) ability to achieve goals and deliver on a plan. The best way to ensure success is constant dialogue and communication with your sub-team leader about what you think you can achieve, and any challenges you’re facing.
Goals and deliverables for the training team will be provided by instructors and are known to be achievable based on prior experience.
Submission and review:#
The deliverables will be submitted via Canvas as a .zip file, and a copy of the project goals document should be included with the submission to aid the reviewers. All submissions should contain a file named “README” that explains the accomplishments in the context of the project goals and points the reviewer to necessary information. Any external deliverables (e.g. websites, Github, etc.) should be clearly referenced. The README can also contain any comments, instructions, or context that may be important for a grader. Only materials included in or referenced in the submission should be used to assess the criteria (e.g. if a group presents something in the VIP meeting but does not include it in the submission then it does not count).
Teamwork and Interaction#
Teamwork and interaction will be graded based on peer evaluations conducted through the VIP website and your participation in peer grading. Note that this can be confusing. Peer evaluations occur twice per semester through the VIP website, while peer grading occurs bi-weekly for documentation. The response rate for peer grading and peer evaluations will account for 40% of the teamwork and interaction grade., while the scores you receive from your teammates in the peer evaluation will account for the other 60% of this grade. Note that you only need to complete peer evals for your sub-team members, but peer grading will be across different subteams and will be explicitly assigned.
Course Format#
The course will by offered in a primarily virtual format. We have multiple online MS students who work in the course, so it is important to provide a mechanism for remote participation. The main lecture will take place virtually, but sub-teams can meet in person and/or use hybrid meetings. For the training group, lectures will pre-recorded and posted to Canvas, and the course time time will serve as an opportunity for questions or help with assignments (a “flipped” classroom). The project groups will use the course time to meet and discuss progress on projects.
Training Schedule#
Week 1 (8/21): Introduction to VIP and projects (Medford/Brabson)
Week 2 (8/28): VIP Subteam pitches (Medford/Timmerman/Yu/Tian)
Week 3 (9/4): Literature searches (Medford) - Bi-weekly Update 1
Week 4 (9/11): Intro to Python and HPC (Brabson)
Week 5 (9/18): Intro to ASE (Brabson) - Bi-weekly Update 2 (Ex. 1.6.1-1.6.5)
Week 6 (9/25): Intro to Density Function Theory I (Bhowmik)
Week 7 (10/2): Midterm Updates (pre-recorded presentations, virtual synchronous Q&A) - Bi-weekly Update 3 (Ex. 2.4.1, 2.5.1, & 3.3.1-3.3.4)
Week 8 (10/9): Intro to Density Functional Theory II (Bhowmik)
Week 9 (10/16): DFT Calculations I (Bhowmik/Brabson) - Bi-weekly Update 4
Week 10 (10/23): DFT Calculations II (Bhowmik/Brabson)
Week 11 (10/30): Projects (Timmerman/Yu/Tian) - Bi-weekly Update 5 (Ex. 5.4.1-5.4.2)
Week 12 (11/6): DFT Project Workshop
Week 13 (11/13): Projects (Timmerman/Yu/Tian) - Bi-weekly Update 6 (DFT Project DUE)
Week 14 (11/20): Final Updates (pre-recorded presentations, virtual synchronous Q&A)
Week 15 (11/28): Thanksgiving Break (No Class)
Week 16 (12/4): Reading Day (No Class)
Changes to Syllabus#
The schedule and syllabus are subject to change. Given that this is a research course, changes are to be expected; however, we will do our best to notify you of any changes and implement them as fairly as possible.