diff --git a/_sources/docs/Exercises_ASE_intro.ipynb b/_sources/docs/Exercises_ASE_intro.ipynb index c29f4da..ac5ea04 100755 --- a/_sources/docs/Exercises_ASE_intro.ipynb +++ b/_sources/docs/Exercises_ASE_intro.ipynb @@ -1,148 +1,148 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "d2fe0ce8-b7ef-40d0-8d03-f85630d3ae8d", - "metadata": {}, - "source": [ - "# Exercises" - ] - }, - { - "cell_type": "markdown", - "id": "58d1f53d-6a3e-4c47-afd6-f7af64c1bd20", - "metadata": {}, - "source": [ - "These exercises cover the basic utilities of the Python Atomic Simulation Environment (ASE)." - ] - }, - { - "cell_type": "markdown", - "id": "2ca9c86a", - "metadata": {}, - "source": [ - "(ase-intro-ex)=\n", - "## Exercise 1" - ] - }, - { - "cell_type": "markdown", - "id": "845c1165-e92b-49ee-a8b1-acfed1ad01d1", - "metadata": {}, - "source": [ - "## Exercise 1" - ] - }, - { - "cell_type": "markdown", - "id": "33441ff2", - "metadata": {}, - "source": [ - "First, be sure you have downloaded ASE using the Anaconda package manager. Import the Atoms and io modules of the ASE package in the code block below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "69ab41ee", - "metadata": {}, - "outputs": [], - "source": [ - "# import statements here\n" - ] - }, - { - "cell_type": "markdown", - "id": "e9199451", - "metadata": {}, - "source": [ - "Next, read in the Ca-LTA Crystallographic Information File (CIF) as an Atoms object using ASE. **Note** This file, along with all other data required for completing these exercises can be found in the `resources/data/` folder. Ca-LTA is a zeolite with gas adsorption applications and will be the focus of several exercises throughout this training course." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ea01a638", - "metadata": {}, - "outputs": [], - "source": [ - "# read in structure here" - ] - }, - { - "cell_type": "markdown", - "id": "80144b04", - "metadata": {}, - "source": [ - "Using built-in ASE tools, print the number of atoms and each atomic identity." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4c6d6fbf", - "metadata": {}, - "outputs": [], - "source": [ - "# code here\n" - ] - }, - { - "cell_type": "markdown", - "id": "18ed8de9", - "metadata": {}, - "source": [ - "Use ASE tools to manually activate periodic boundary conditions in all three directions. This will be important when performing ASE calculations in the next module." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "69d12429", - "metadata": {}, - "outputs": [], - "source": [ - "# code here\n" - ] - }, - { - "cell_type": "markdown", - "id": "5254def4", - "metadata": {}, - "source": [ - "Lastly, print the unit cell volume to the console." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c570b68f", - "metadata": {}, - "outputs": [], - "source": [ - "# code here\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python (sparc1.2)", - "language": "python", - "name": "my-new-env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d2fe0ce8-b7ef-40d0-8d03-f85630d3ae8d", + "metadata": {}, + "source": [ + "# Exercises" + ] + }, + { + "cell_type": "markdown", + "id": "58d1f53d-6a3e-4c47-afd6-f7af64c1bd20", + "metadata": {}, + "source": [ + "These exercises cover the basic utilities of the Python Atomic Simulation Environment (ASE)." + ] + }, + { + "cell_type": "markdown", + "id": "2ca9c86a", + "metadata": {}, + "source": [ + "(ase-intro-ex)=\n", + "## Exercise 1" + ] + }, + { + "cell_type": "markdown", + "id": "845c1165-e92b-49ee-a8b1-acfed1ad01d1", + "metadata": {}, + "source": [ + "## Exercise 1" + ] + }, + { + "cell_type": "markdown", + "id": "33441ff2", + "metadata": {}, + "source": [ + "First, be sure you have downloaded ASE using the Anaconda package manager. Import the Atoms and io modules of the ASE package in the code block below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "69ab41ee", + "metadata": {}, + "outputs": [], + "source": [ + "# import statements here\n" + ] + }, + { + "cell_type": "markdown", + "id": "e9199451", + "metadata": {}, + "source": [ + "Next, read in the Ca-LTA Crystallographic Information File (CIF) as an Atoms object using ASE. **Note** This file, along with all other data required for completing these exercises can be found in the `resources/data/` folder. Ca-LTA is a zeolite with gas adsorption applications and will be the focus of several exercises throughout this training course." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea01a638", + "metadata": {}, + "outputs": [], + "source": [ + "# read in structure here" + ] + }, + { + "cell_type": "markdown", + "id": "80144b04", + "metadata": {}, + "source": [ + "Using built-in ASE tools, print the number of atoms and each atomic identity." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4c6d6fbf", + "metadata": {}, + "outputs": [], + "source": [ + "# code here\n" + ] + }, + { + "cell_type": "markdown", + "id": "18ed8de9", + "metadata": {}, + "source": [ + "Use ASE tools to manually activate periodic boundary conditions in all three directions. This will be important when performing ASE calculations in the next module." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "69d12429", + "metadata": {}, + "outputs": [], + "source": [ + "# code here\n" + ] + }, + { + "cell_type": "markdown", + "id": "5254def4", + "metadata": {}, + "source": [ + "Lastly, print the unit cell volume to the console." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c570b68f", + "metadata": {}, + "outputs": [], + "source": [ + "# code here\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (sparc1.2)", + "language": "python", + "name": "my-new-env" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_sources/docs/Exercises_DFT_applications.ipynb b/_sources/docs/Exercises_DFT_applications.ipynb index e8a68d2..bf26602 100755 --- a/_sources/docs/Exercises_DFT_applications.ipynb +++ b/_sources/docs/Exercises_DFT_applications.ipynb @@ -1,204 +1,204 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "d2fe0ce8-b7ef-40d0-8d03-f85630d3ae8d", - "metadata": {}, - "source": [ - "# Exercises" - ] - }, - { - "cell_type": "markdown", - "id": "845c1165-e92b-49ee-a8b1-acfed1ad01d1", - "metadata": {}, - "source": [ - "(dft-1-ex)=\n", - "## Exercise 1" - ] - }, - { - "cell_type": "markdown", - "id": "33441ff2", - "metadata": {}, - "source": [ - "One major application of density functional theory is performing structural relaxation (i.e., geometric optimization).
Begin by building a linear CO2 molecule in ASE with a C–O bond length of 1.8 Å." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b3d2476b", - "metadata": {}, - "outputs": [], - "source": [ - "from ase import Atoms\n", - "\n", - "#Code here" - ] - }, - { - "cell_type": "markdown", - "id": "03e0c4ba", - "metadata": {}, - "source": [ - "Next, relax your molecule using SPARC with parameters of your choosing. Confirm that your relaxed C–O bond length matches that from experimentation. Check the manual for the appropriate flags for relaxation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e7ecc3db", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from sparc import SPARC\n", - "from ase.units import Bohr,Hartree,mol,kcal,kJ,eV\n", - "from ase.io import read, write\n", - "\n", - "#Code here" - ] - }, - { - "cell_type": "markdown", - "id": "db771ffe", - "metadata": {}, - "source": [ - "(dft-2-ex)=\n", - "## Exercise 2" - ] - }, - { - "cell_type": "markdown", - "id": "ba045e75", - "metadata": {}, - "source": [ - "In this exercise, we will consider the adsorption of an atomic oxygen on Pt(111) surface, instead of O2 molecule.\n", - "Consider fcc and hcp active sites. Determine the most stable adsorption site of an oxygen atom on Pt(111) surface and the corresponding adsorption energy when the surface is saturated with one monolayer of oxygens (in this case, one O atom for each Pt surface atom)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c2cf958a", - "metadata": {}, - "outputs": [], - "source": [ - "from ase import Atoms, Atom\n", - "from ase.build import bulk, molecule, surface, add_adsorbate, fcc111, fcc100\n", - "from ase.constraints import FixAtoms\n", - "from ase.io import read\n", - "\n", - "#Code here" - ] - }, - { - "cell_type": "markdown", - "id": "7d656717", - "metadata": {}, - "source": [ - "## Exercise 3" - ] - }, - { - "cell_type": "markdown", - "id": "54072710", - "metadata": {}, - "source": [ - "CO2 can adsorb in Ca-LTA in one of three positions shown in the figure below. Note that the large teal atoms are calcium." - ] - }, - { - "cell_type": "markdown", - "id": "1b693c79-acbe-40c8-b0cd-2fe96d99e2c4", - "metadata": {}, - "source": [ - "![MOFImage](./image.png)" - ] - }, - { - "cell_type": "markdown", - "id": "5bacd640", - "metadata": {}, - "source": [ - "Using a DFT package and calculation parameters of your choice, compute the CO2 adsorption energy for each of the three positions in Ca-LTA. *This exercise requires that you manually add CO2 molecules into the Ca-LTA framework and that you perform several structural relaxations at different geometries. You can fix the atomic positions of Ca-LTA to reduce the computational cost* Comment on which of the active sites is the most stable. Based on the energies, is it reasonable to assume a single active site dominates? How would you expect the results to shift as the active sites become saturated?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c13e8f2c", - "metadata": {}, - "outputs": [], - "source": [ - "# First, relax the pristine MOF\n", - "\n", - "import os\n", - "from ase import Atoms, io\n", - "from ase.io import read, write\n", - "from ase.build import bulk, molecule, surface, add_adsorbate\n", - "from ase.units import Bohr,Hartree,mol,kcal,kJ,eV\n", - "#These constraints allow you to fix atoms. See ASE documentation\n", - "from ase.constraints import FixAtoms\n", - "from sparc import SPARC\n", - "\n", - "#Code here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a8fb3b7a", - "metadata": {}, - "outputs": [], - "source": [ - "# Add CO2 to relaxed pristine structures\n", - "\n", - "from sparc import SPARC\n", - "from ase import Atom\n", - "from ase.build import bulk, molecule, surface, add_adsorbate\n", - "from ase.units import Bohr,Hartree,mol,kcal,kJ,eV\n", - "from ase.io import read, write\n", - "import numpy as np\n", - "\n", - "#Code here" - ] - }, - { - "cell_type": "markdown", - "id": "78d648bd-6407-43bb-806e-31fc86a96480", - "metadata": {}, - "source": [ - "**Responses here**" - ] - }, - { - "cell_type": "markdown", - "id": "65c2db5d-1e54-4119-a4bb-4eea04e65a19", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d2fe0ce8-b7ef-40d0-8d03-f85630d3ae8d", + "metadata": {}, + "source": [ + "# Exercises" + ] + }, + { + "cell_type": "markdown", + "id": "845c1165-e92b-49ee-a8b1-acfed1ad01d1", + "metadata": {}, + "source": [ + "(dft-1-ex)=\n", + "## Exercise 1" + ] + }, + { + "cell_type": "markdown", + "id": "33441ff2", + "metadata": {}, + "source": [ + "One major application of density functional theory is performing structural relaxation (i.e., geometric optimization).
Begin by building a linear CO2 molecule in ASE with a C–O bond length of 1.8 Å." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b3d2476b", + "metadata": {}, + "outputs": [], + "source": [ + "from ase import Atoms\n", + "\n", + "#Code here" + ] + }, + { + "cell_type": "markdown", + "id": "03e0c4ba", + "metadata": {}, + "source": [ + "Next, relax your molecule using SPARC with parameters of your choosing. Confirm that your relaxed C–O bond length matches that from experimentation. Check the manual for the appropriate flags for relaxation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e7ecc3db", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from sparc import SPARC\n", + "from ase.units import Bohr,Hartree,mol,kcal,kJ,eV\n", + "from ase.io import read, write\n", + "\n", + "#Code here" + ] + }, + { + "cell_type": "markdown", + "id": "db771ffe", + "metadata": {}, + "source": [ + "(dft-2-ex)=\n", + "## Exercise 2" + ] + }, + { + "cell_type": "markdown", + "id": "ba045e75", + "metadata": {}, + "source": [ + "In this exercise, we will consider the adsorption of an atomic oxygen on Pt(111) surface, instead of O2 molecule.\n", + "Consider fcc and hcp active sites. Determine the most stable adsorption site of an oxygen atom on Pt(111) surface and the corresponding adsorption energy when the surface is saturated with one monolayer of oxygens (in this case, one O atom for each Pt surface atom)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2cf958a", + "metadata": {}, + "outputs": [], + "source": [ + "from ase import Atoms, Atom\n", + "from ase.build import bulk, molecule, surface, add_adsorbate, fcc111, fcc100\n", + "from ase.constraints import FixAtoms\n", + "from ase.io import read\n", + "\n", + "#Code here" + ] + }, + { + "cell_type": "markdown", + "id": "7d656717", + "metadata": {}, + "source": [ + "## Exercise 3" + ] + }, + { + "cell_type": "markdown", + "id": "54072710", + "metadata": {}, + "source": [ + "CO2 can adsorb in Ca-LTA in one of three positions shown in the figure below. Note that the large teal atoms are calcium." + ] + }, + { + "cell_type": "markdown", + "id": "1b693c79-acbe-40c8-b0cd-2fe96d99e2c4", + "metadata": {}, + "source": [ + "![MOFImage](./image.png)" + ] + }, + { + "cell_type": "markdown", + "id": "5bacd640", + "metadata": {}, + "source": [ + "Using a DFT package and calculation parameters of your choice, compute the CO2 adsorption energy for each of the three positions in Ca-LTA. *This exercise requires that you manually add CO2 molecules into the Ca-LTA framework and that you perform several structural relaxations at different geometries. You can fix the atomic positions of Ca-LTA to reduce the computational cost* Comment on which of the active sites is the most stable. Based on the energies, is it reasonable to assume a single active site dominates? How would you expect the results to shift as the active sites become saturated?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c13e8f2c", + "metadata": {}, + "outputs": [], + "source": [ + "# First, relax the pristine MOF\n", + "\n", + "import os\n", + "from ase import Atoms, io\n", + "from ase.io import read, write\n", + "from ase.build import bulk, molecule, surface, add_adsorbate\n", + "from ase.units import Bohr,Hartree,mol,kcal,kJ,eV\n", + "#These constraints allow you to fix atoms. See ASE documentation\n", + "from ase.constraints import FixAtoms\n", + "from sparc import SPARC\n", + "\n", + "#Code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a8fb3b7a", + "metadata": {}, + "outputs": [], + "source": [ + "# Add CO2 to relaxed pristine structures\n", + "\n", + "from sparc import SPARC\n", + "from ase import Atom\n", + "from ase.build import bulk, molecule, surface, add_adsorbate\n", + "from ase.units import Bohr,Hartree,mol,kcal,kJ,eV\n", + "from ase.io import read, write\n", + "import numpy as np\n", + "\n", + "#Code here" + ] + }, + { + "cell_type": "markdown", + "id": "78d648bd-6407-43bb-806e-31fc86a96480", + "metadata": {}, + "source": [ + "**Responses here**" + ] + }, + { + "cell_type": "markdown", + "id": "65c2db5d-1e54-4119-a4bb-4eea04e65a19", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_sources/docs/VIP_Overview.ipynb b/_sources/docs/VIP_Overview.ipynb index 7c43020..fa80022 100755 --- a/_sources/docs/VIP_Overview.ipynb +++ b/_sources/docs/VIP_Overview.ipynb @@ -1,236 +1,245 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Big Data & Quantum Mechanics\n", - " \n", - "\n", - "## Overview of Medford Group research\n", - "\n", - "![MedfordImg](./medford_group_bg.png)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## About Prof. AJ Medford\n", - "\n", - "- Started as a professor (and this VIP course) in Spring 2017.\n", - "- Experience in developing and contributing to several open-source software packages (CatMAP, ElectroLens, TAPSolver, SPARC, AMPTorch)\n", - "- Instructor for \"Data Analytics for Chemical Engineers\" and Numerical Methods.\n", - "- Interest in applying data science techniques to problems in quantum chemistry and physics.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introductions\n", - "\n", - "We will go around the class and introduce ourselves to the everyone. When it is your turn to speak, tell everyone your *preferred name, major, and something **boring** about yourself*." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## How does VIP work?\n", - "\n", - "The premise of VIP is teams working on projects. Much like a real-world engineering team, individual members work on different aspects of the project. Team members range from sophomores through graduate students, from first-time participants to students who have been involved for four or more semesters. Some students take the course for one credit, and others take it for two credits; naturally, the bar will be higher for those taking it for two credits.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## How is VIP graded?\n", - "\n", - "You will receive a grade for the course based on three criteria:\n", - "\n", - "- Documentation (33.3%): Based on biweekly updates of progress on tasks.\n", - "- Personal Accomplishments (33.3%): Based on how well you achieve your research goals.\n", - "- Teamwork and Participation (33.3%): Peer evaluations will be used to establish how well you work on a team.\n", - "\n", - "Grading process:\n", - "- Bi-weekly on Thursday: Submit \"bi-weekly update\" and literature review to Canvas. Complete peer grading (instructions in syllabus).\n", - "\n", - "- Midterm: Submit personal accomplishment documentation to Canvas. Complete peer evaluations. Complete peer grading. *Note for returning students* This grade **will** be counted towards your final grade in the course.\n", - "\n", - "- Final: Identical to midterm. Final grade will be a weighted average of the midterm and final submissions.\n", - "\n", - "The following deliverables are expected at the midterm and final evaluations. Note that the \"personal accomplishment\" documentation will be graded using a combination of peer grading and instructor grading, so you will also need to complete the peer grading at each point.\n", - "\n", - "- Deliverables: \n", - " - Compiled bi-weekly update\n", - " - Personal accomplishment documentation\n", - " - Peer grading\n", - " - Peer evaluation\n", - " - Demonstrated completion of all assigned training materials\n", - "\n", - "See [syllabus](VIP_syllabus.md) for more details." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## VIP is not like a regular course \n", - "\n", - "Regular courses have a clear direction:\n", - "\n", - "![MarioImg](./mario.png)\n", - "\n", - "VIP lets you choose your own adventure:\n", - "\n", - "![ZeldaImg](./zelda.jpeg)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Group Communication:\n", - "\n", - "- Slack group used for all communication, join using [this link](https://join.slack.com/t/slack-ymv5720/shared_invite/zt-21v1xmbap-5FQMR2rgE0B5krwMZPFyBQ)\n", - "\t- training: discussion related to training project.\n", - "\t- general: channel for general discussions with the whole group.\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Team Structure : New Students\n", - "\n", - "- Complete training materials. Consists of introductions to computational tools, density functional theory, and machine learning concepts.\n", - "\n", - "New students will join the \"Training\" sub-team. You will work individually to complete all the tasks, but should meet regularly with your teammates and graduate advisor to touch base on progress, ask questions, and get help with anything you are stuck on. The training materials should be completed shortly after the midterm. After the training course, you are free to join one of the sub-teams directly, or \"sample\" the available teams each week, but keep in mind that the expectation is that you join just one team by the end of the semester and begin working on a specific project. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Team Structure: Returning Students, OMSCS Students, post Training New Students\n", - "\n", - "- Returning students will join one of the \"sub-teams\" described below, or work on \"independent study\" projects.\n", - "- OMSCS students can decide whether to complete the training exercises or join a sub-team (or both)\n", - "- New students will be expected to join one of the sub-teams after the midterm\n", - "\n", - "### Sub-teams\n", - "- There are several sub-teams, each of which will function as a small research group advised by a graduate mentor\n", - " * **DFT Modeling of Surface Reactions** - This team will focus on the computational investigation of surface reactions using SPARC DFT code, as well as testing and improving the SPARC code suite.\n", - " * **Machine Learning for Catalysis** - This team will focus on developing ML tools for catalysis, with a focus on LLMs for both catalysis and DFT workflow automation (ChatDFT).\n", - " * **Machine Learning for Catalyst Discovery in Waste Sludge Chemistry** - This team will focus on utilizing ML tools to generate surface adsorption energy predictions and data analytics to provide insights for waste sludge chemistry. \n", - " \n", - "- Students should work on self-defined individual tasks within the scope of a sub-team\n", - " * Each student should have a clearly-defined task that they are working independently on. This task should be self-determined and within the scope of the broader goal of the sub-team based on consultations with other sub-team members and the graduate student advisor.\n", - " * Students may work together on a given task or direction, but should also have individual goals.\n", - " * Students should regularly communicate with their sub-team to (1) coordinate progress on individual tasks to work toward the larger goal of the team, (2) ask for and provide assistance to other sub-team members, and (3) seek advice from and provide updates to the sub-team graduate mentor.\n", - " * It is fully expected that the goals of research tasks change throughout the semester. The goals document can be updated at any time up to 2 weeks prior to the end of the semester. Students should revise goals as needed to ensure they are achievable.\n", - " * Note that the achievements grade is determined by your sub-team advisor. Different advisors may have different expectations and organizational standards. You should be sure to clearly communicate with your subteam advisor to be sure that you are meeting their expectations." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Standard meeting format\n", - "\n", - "Subsequent meetings will follow one of three formats. We will start each meeting virtually in this Zoom room regardless of the format.\n", - "\n", - "* Lecture meetings: The main lecture will be used to briefly discuss logistics before breaking into sub-groups. All training students should plan on attending the training breakout session for discussion regarding that week's lecture(s), while other subgroups will attend their own breakout rooms. Sub-groups where all (or some) of the students are on campus may elect to meet in person or in a hybrid mode based on the preference of group members.\n", - "\n", - "\n", - "* Update meetings: For midterm and final updates, each team will post a 10-15 minute update presentation to Canvas, and each student will be assigned 3 update presentations to watch and provide peer reviews before class. During the class time, all students are expected to be present, and we will go through each group to field questions and discuss their work. Any remaining time will be used for sub-group meetings. \n", - "\n", - "\n", - "* Workshops: No official lecture topic. The entire lecture will be used as unstructured time to work on projects and interact with mentors and instructors. Training students should use this as an opportunity to join the breakout room of the subteams they are most interested in joining.\n", - "\n", - "**Note:** If no member of your sub-team is present for the synchronous lecture, then everyone from the group will lose 1/2 point (out of 5) from the teamwork grade. If you cannot attend please coordinate with your group and confirm with an instructor at least 24 hours ahead of time if nobody from your group will be there." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Lecture schedule and syllabus\n", - "\n", - "The course [syllabus](VIP_syllabus.md) is available within this book, and includes a list of all the lecture topics and dates." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Week 1 Assignment\n", - "\n", - "- Join the [Slack channel](https://join.slack.com/t/slack-ymv5720/shared_invite/zt-21v1xmbap-5FQMR2rgE0B5krwMZPFyBQ)\n", - "- Start discussion for selecting your group or sub-team project\n", - "- Install necessary software following instructions below." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Software Installation:\n", - "\n", - "### Install Anaconda3\n", - "\n", - "We'll be using Python3 and Jupyter notebooks extenstively in this class. To access this easily, we'll need to install anaconda3. To do that, go to the anaconda website below and simply follow the buttons to download and install it (ensure that you're downloading the correct version for your operating system.)\n", - "\n", - "https://www.anaconda.com/distribution/\n", - "\n", - "### Ensure you can access a linux/unix prompt\n", - "\n", - "#### Windows users:\n", - "Please install the windows ubuntu subsystem using these instructions:\n", - "\n", - "https://docs.microsoft.com/en-us/windows/wsl/install-win10\n", - "\n", - "#### Mac users:\n", - "Be sure you can open a [terminal](https://www.youtube.com/watch?v=zw7Nd67_aFw)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.11" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": true, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": true, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Big Data & Quantum Mechanics\n", + " \n", + "\n", + "## Overview of Medford Group research\n", + "\n", + "![MedfordImg](./medford_group_bg.png)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## About Prof. AJ Medford\n", + "\n", + "- Started as a professor (and this VIP course) in Spring 2017.\n", + "- Experience in developing and contributing to several open-source software packages (CatMAP, ElectroLens, TAPSolver, SPARC, AMPTorch)\n", + "- Instructor for \"Data Analytics for Chemical Engineers\" and Numerical Methods.\n", + "- Interest in applying data science techniques to problems in quantum chemistry and physics.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introductions\n", + "\n", + "We will go around the class and introduce ourselves to the everyone. When it is your turn to speak, tell everyone your *preferred name, major, and something **random** about yourself*." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How does VIP work?\n", + "\n", + "The premise of VIP is teams working on projects. Much like a real-world engineering team, individual members work on different aspects of the project. Team members range from sophomores through graduate students, from first-time participants to students who have been involved for four or more semesters. Some students take the course for one credit, and others take it for two credits; naturally, the bar will be higher for those taking it for two credits.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How is VIP graded?\n", + "\n", + "You will receive a grade for the course based on three criteria:\n", + "\n", + "- Documentation (33.3%): Based on biweekly updates of progress on tasks.\n", + "- Personal Accomplishments (33.3%): Based on how well you achieve your research goals.\n", + "- Teamwork and Participation (33.3%): Peer evaluations will be used to establish how well you work on a team.\n", + "\n", + "Grading process:\n", + "- Bi-weekly on Thursday: Submit \"bi-weekly update\" and literature review to Canvas. Complete peer grading (instructions in syllabus).\n", + "\n", + "- Midterm: Submit personal accomplishment documentation to Canvas. Complete peer evaluations. Complete peer grading. *Note for returning students* This grade **will** be counted towards your final grade in the course.\n", + "\n", + "- Final: Identical to midterm. Final grade will be a weighted average of the midterm and final submissions.\n", + "\n", + "The following deliverables are expected at the midterm and final evaluations. Note that the \"personal accomplishment\" documentation will be graded using a combination of peer grading and instructor grading, so you will also need to complete the peer grading at each point.\n", + "\n", + "- Deliverables: \n", + " - Compiled bi-weekly update\n", + " - Personal accomplishment documentation\n", + " - Peer grading\n", + " - Peer evaluation\n", + " - Demonstrated completion of all assigned training materials\n", + "\n", + "See [syllabus](VIP_syllabus.md) for more details." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## VIP is not like a regular course \n", + "\n", + "Regular courses have a clear direction:\n", + "\n", + "![MarioImg](./mario.png)\n", + "\n", + "VIP lets you choose your own adventure:\n", + "\n", + "![ZeldaImg](./zelda.jpeg)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Group Communication:\n", + "\n", + "- Slack group used for all communication, join using [this link](https://join.slack.com/t/slack-ymv5720/shared_invite/zt-21v1xmbap-5FQMR2rgE0B5krwMZPFyBQ)\n", + "\t- training: discussion related to training project.\n", + "\t- general: channel for general discussions with the whole group.\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overarching Project: ChatDFT development\n", + "\n", + "This semester, we are starting to work on a new large-language model (LLM) based tool that we hope will make it easier for students to learn how to do DFT calculations. This is also partially educational research, and we will send out various surveys throughout the semester. The bot, named \"ChatDFT-F24\" will be accessible via Slack, and is based off of ChatGPT. This semester, all projects will involve performing DFT simulations with the aid of ChatDFT to study reproducibility in DFT and how LLMs can assist in performing scientific computing tasks. Note that all converations with ChatDFT will be recorded and may be reviewed and/or used for fine-tuning or development of future versions of ChatDFT. We also appreciate any feedback you have about ChatDFT, including best practices for prompt engineering, ideas about how to improve the architecture, or various failure modes that you experience. The tool is far from perfect, and like any LLM it should not be blindly trusted!\n", + "\n", + "All subteams will also work with the SPARC DFT code, a recent DFT package developed by Prof. Suryanarayana at Georgia Tech, designed for massively parallel calculations. This code is very powerful, but relatively new, and thus not as familiar for LLM tools. We hope that through this project we can help tailor an LLM interface that allows new users to quickly learn to use SPARC to perform reliable calculations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Team Structure : New Students\n", + "\n", + "All new undergraduate students will automatically join the training subteam:\n", + "\n", + "* **Training (led by Logan Brabson and Sayan Bhowmik)** - All new undergraduate students must complete a training program that involves the basics of high-performance computing, common quantum-mechanical techniques such as Density Function Theory (DFT), and the development and use of machine learning techniques such as neural networks. DFT software packages are well-established, but require the use of supercomputing resources and must be converged with respect to several numerical parameters. The training will cover approximately 10 weeks and is highly represnetative of training programs for new graduate students in this field." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Team Structure: Returning Students, OMSCS Students, post Training New Students\n", + "\n", + "Returning students will join one of the \"sub-teams\" described below, and OMSCS students can decide whether to complete the training exercises or join a sub-team (or both)\n", + "\n", + "### Sub-teams\n", + "- There are several sub-teams, each of which will function as a small research group advised by a graduate mentor. The main tasks in each sub-team will be calculation of adsorption energies, but each team will focus on different systems and mentors may focus on different aspects (e.g. reproducibility, accuracy at different levels of theory, etc.).\n", + " * **Simulation of transition metal surfaces (led by Neung-Kyung Yu)** - Transition metals are some of the most common catalysts, and adsorption on transition metals has been widely studied. However, there are still open questions about the accuracy of these models since adsorption energies are difficult to measure, and the vast literature provides lots of opportunities to study reproducibility. This team will focus on calculating some well-known adsorption energies and evaluating accuracy and reproducibility.\n", + " * **Adsorption on electrochemical oxide surfaces (led by Kaylee Tian)** - Electrochemistry and electrocatalysis are promising sustainable alternatives for a wide range of processes including fuel production and waste treatment. However, calculating adsorption energies on oxides can be challenging due to complex electronic structure (magnetism, localized electrons) and the fact that many metal oxides are highly reactive and can change under reaction conditions. This team will focus on the influence of different atomic configurations of oxide surfaces and different DFT settings and initializations on the adsorption energies of intermediates related to electrocatalytric nutrient recovery from waste sludge.\n", + " * **Simulations of Nano-porous Materials (led by Lucas Timmerman)** - This team will focus on electronic structure theory (EST) calculations for zeolites and metal organic frameworks. These will primarily consist of adsorption energy calculations for materials and adsorbates of interest for sustainability applications, including direct air capture and biomass conversion. Outcomes include basic proficiency in EST calculations as well as a fundamental understanding of the chemistry and physics concepts underpinning at least one of the materials listed above.\n", + " \n", + "- Students should work on self-defined individual tasks within the scope of a sub-team\n", + " * Each student should have a clearly-defined task that they are working independently on. This task should be self-determined and within the scope of the broader goal of the sub-team based on consultations with other sub-team members and the graduate student advisor.\n", + " * Students may work together on a given task or direction, but should also have individual goals.\n", + " * Students should regularly communicate with their sub-team to (1) coordinate progress on individual tasks to work toward the larger goal of the team, (2) ask for and provide assistance to other sub-team members, and (3) seek advice from and provide updates to the sub-team graduate mentor.\n", + " * It is fully expected that the goals of research tasks change throughout the semester. The goals document can be updated at any time up to 2 weeks prior to the end of the semester. Students should revise goals as needed to ensure they are achievable.\n", + " * Note that the achievements grade is determined by your sub-team advisor. Different advisors may have different expectations and organizational standards. You should be sure to clearly communicate with your subteam advisor to be sure that you are meeting their expectations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Standard meeting format\n", + "\n", + "Subsequent meetings will follow one of three formats. We will start each meeting virtually in this Zoom room regardless of the format.\n", + "\n", + "* Lecture meetings: The main lecture will be used to briefly discuss logistics before breaking into sub-groups. All training students should plan on attending the training breakout session for discussion regarding that week's lecture(s), while other subgroups will attend their own breakout rooms. Sub-groups where all (or some) of the students are on campus may elect to meet in person or in a hybrid mode based on the preference of group members.\n", + "\n", + "\n", + "* Update meetings: For midterm and final updates, each team will post a 10-15 minute update presentation to Canvas, and each student will be assigned 3 update presentations to watch and provide peer reviews before class. During the class time, all students are expected to be present, and we will go through each group to field questions and discuss their work. Any remaining time will be used for sub-group meetings. \n", + "\n", + "\n", + "* Workshops: No official lecture topic. The entire lecture will be used as unstructured time to work on projects and interact with mentors and instructors. Training students should use this as an opportunity to join the breakout room of the subteams they are most interested in joining.\n", + "\n", + "**Note:** If no member of your sub-team is present for the synchronous lecture, then everyone from the group will lose 1/2 point (out of 5) from the teamwork grade. If you cannot attend please coordinate with your group and confirm with an instructor at least 24 hours ahead of time if nobody from your group will be there." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lecture schedule and syllabus\n", + "\n", + "The course [syllabus](VIP_syllabus.md) is available within this book, and includes a list of all the lecture topics and dates." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Week 1 Assignment\n", + "\n", + "- Join the [Slack channel](https://join.slack.com/t/slack-ymv5720/shared_invite/zt-21v1xmbap-5FQMR2rgE0B5krwMZPFyBQ)\n", + "- Start discussion for selecting your group or sub-team project\n", + "- Install necessary software following instructions below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Software Installation:\n", + "\n", + "### Install Anaconda3\n", + "\n", + "We'll be using Python3 and Jupyter notebooks extenstively in this class. To access this easily, we'll need to install anaconda3. To do that, go to the anaconda website below and simply follow the buttons to download and install it (ensure that you're downloading the correct version for your operating system.)\n", + "\n", + "https://www.anaconda.com/distribution/\n", + "\n", + "### Ensure you can access a linux/unix prompt\n", + "\n", + "#### Windows users:\n", + "Please install the windows ubuntu subsystem using these instructions:\n", + "\n", + "https://docs.microsoft.com/en-us/windows/wsl/install-win10\n", + "\n", + "#### Mac users:\n", + "Be sure you can open a [terminal](https://www.youtube.com/watch?v=zw7Nd67_aFw)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": true, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/_sources/docs/VIP_syllabus.md b/_sources/docs/VIP_syllabus.md index 67cb2ed..10633f5 100755 --- a/_sources/docs/VIP_syllabus.md +++ b/_sources/docs/VIP_syllabus.md @@ -68,28 +68,9 @@ 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: +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) -- Training (led by Logan Brabson and Sayan Bhowmik): All new undergraduate students must complete a training program that - involves the basics of high-performance computing, common quantum-mechanical techniques - such as Density Function Theory (DFT), and the development and use of machine learning - techniques such as neural networks. DFT software packages are well-established, but - require the use of supercomputing resources and must be converged with respect to - several numerical parameters. The training will cover approximately 10 weeks and is - highly represnetative of training programs for new graduate students in this field. - -- Machine Learning for Catalysis (led by Lucas Timmerman): This team will focus on developing ML tools for catalysis, with a focus on large language models (LLMs) for both catalysis and DFT workflow automation (ChatDFT). Students will develop chat bots for helping new DFT users learn the basics of DFT calculations and for helping existing users improve the reproducibility of their calculations. Familiarity with machine learning and a desire to work with LLMs will be useful. This team will also work on testing models as they are implemented and will involve some DFT work. - -- DFT Modeling of Surface Reactions (led by Neung-Kyung Yu): This project involves using Density Functional Theory (DFT) to model and investigate surface reactions. DFT is a widely-used computational approach for simulating atomistic models. The project will employ the [SPARC code](https://www.sparc-x.com/), a recent DFT package developed by Prof. Suryanarayana at Georgia Tech, designed for massive parallel calculations. In this project, we will go beyond the scope of the DFT training materials and apply DFT to study fascinating surface reactions. Prior knowledge of the topics isn't required, but an interest in computational investigation of chemical reactions is needed as the project will involve greater complexity in both DFT and chemistry. Additionally, the project will involve testing new features in SPARC and enhancing its Python API. - -- Machine Learning for Catalyst Discovery in Waste Sludge Chemistry (led by Kaylee Tian): This project delves into the intersection of computational cheminformatics and data analytics. The project focuses on utilizing ML tools to generate surface adsorption energy predictions and data analytics to provide insights for waste sludge chemistry. In this project, the students will utilize existing machine learning models published by [The Open Catalyst Project](https://opencatalystproject.org/) to predict adsorption energy of potential chemicals present in a waste sludge. The Open Catalyst Project aims to use machine learning for approximating quantum mechanical simulations (density functional theory) and discovering new catalysts. Then using data analytics and visualization techniques, the students will draw meaningful insights on material's reactivity relations. The students will gain hands on experience in python as well as experience in data analytics. Students with prior experience in python programming will be prefered. - - -Returning VIP students who previously started work on a project may choose to continue -building on their existing work isntead of joining one of the above subgroups. -If you are a returning student and are interested in an "independent study" project you should -contact Dr. Medford as soon as possible to initiate discussions on what is within scope for -the course and what is feasible given timelines and resources. Course Logistics ================ @@ -103,9 +84,9 @@ submission of assignments: assignments and peer grading. - Slack: The group Slack channel is the preferred method of - communication with instructors and graduate students. You are - responsible for anything that is announced in the `vip` - Slack channel. You can join the Slack channel via [this link]( https://join.slack.com/t/slack-ymv5720/shared_invite/zt-21v1xmbap-5FQMR2rgE0B5krwMZPFyBQ ). + communication with instructors and graduate students. To join the channel + login to the [GT Slack enterprise account](https://gatech.enterprise.slack.com/), + then search for the "Big Data & Quantum Mechanics (VIP)" workspace and ask to join. Course Objectives ================= @@ -185,9 +166,6 @@ Personal Accomplishments Personal accomplishments will be measured by self-defined goals and will be assessed by each student's respective sub-team mentor. -Within the **first three weeks** of the semester students will work with their -sub-team mentor to define midterm and -semester goals for their specific tasks within their sub-team. 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 @@ -251,7 +229,7 @@ assigned. Course Format ------------- -The course will by offered in a hybrid format. We have multiple online MS students who work in the course, so it is important to provide a mechanism for remote participation. +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. @@ -290,6 +268,7 @@ Week 15 (11/28): Thanksgiving Break (No Class) Week 16 (12/4): Reading Day (No Class) + Changes to Syllabus =================== diff --git a/docs/VIP_Overview.html b/docs/VIP_Overview.html index 047ee26..a37da07 100755 --- a/docs/VIP_Overview.html +++ b/docs/VIP_Overview.html @@ -399,6 +399,7 @@

Contents

  • How is VIP graded?
  • VIP is not like a regular course
  • Group Communication:
  • +
  • Overarching Project: ChatDFT development
  • Team Structure : New Students
  • Team Structure: Returning Students, OMSCS Students, post Training New Students +
    +

    Overarching Project: ChatDFT development#

    +

    This semester, we are starting to work on a new large-language model (LLM) based tool that we hope will make it easier for students to learn how to do DFT calculations. This is also partially educational research, and we will send out various surveys throughout the semester. The bot, named “ChatDFT-F24” will be accessible via Slack, and is based off of ChatGPT. This semester, all projects will involve performing DFT simulations with the aid of ChatDFT to study reproducibility in DFT and how LLMs can assist in performing scientific computing tasks. Note that all converations with ChatDFT will be recorded and may be reviewed and/or used for fine-tuning or development of future versions of ChatDFT. We also appreciate any feedback you have about ChatDFT, including best practices for prompt engineering, ideas about how to improve the architecture, or various failure modes that you experience. The tool is far from perfect, and like any LLM it should not be blindly trusted!

    +

    All subteams will also work with the SPARC DFT code, a recent DFT package developed by Prof. Suryanarayana at Georgia Tech, designed for massively parallel calculations. This code is very powerful, but relatively new, and thus not as familiar for LLM tools. We hope that through this project we can help tailor an LLM interface that allows new users to quickly learn to use SPARC to perform reliable calculations.

    +

    Team Structure : New Students#

    +

    All new undergraduate students will automatically join the training subteam:

      -
    • Complete training materials. Consists of introductions to computational tools, density functional theory, and machine learning concepts.

    • +
    • Training (led by Logan Brabson and Sayan Bhowmik) - All new undergraduate students must complete a training program that involves the basics of high-performance computing, common quantum-mechanical techniques such as Density Function Theory (DFT), and the development and use of machine learning techniques such as neural networks. DFT software packages are well-established, but require the use of supercomputing resources and must be converged with respect to several numerical parameters. The training will cover approximately 10 weeks and is highly represnetative of training programs for new graduate students in this field.

    -

    New students will join the “Training” sub-team. You will work individually to complete all the tasks, but should meet regularly with your teammates and graduate advisor to touch base on progress, ask questions, and get help with anything you are stuck on. The training materials should be completed shortly after the midterm. After the training course, you are free to join one of the sub-teams directly, or “sample” the available teams each week, but keep in mind that the expectation is that you join just one team by the end of the semester and begin working on a specific project.

    Team Structure: Returning Students, OMSCS Students, post Training New Students#

    -
      -
    • Returning students will join one of the “sub-teams” described below, or work on “independent study” projects.

    • -
    • OMSCS students can decide whether to complete the training exercises or join a sub-team (or both)

    • -
    • New students will be expected to join one of the sub-teams after the midterm

    • -
    +

    Returning students will join one of the “sub-teams” described below, and OMSCS students can decide whether to complete the training exercises or join a sub-team (or both)

    Sub-teams#

    @@ -503,9 +487,9 @@

    Course Logisticsvip -Slack channel. You can join the Slack channel via this link.

  • +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.

    @@ -568,9 +552,6 @@

    Documentation#

    Personal accomplishments will be measured by self-defined goals and will be assessed by each student’s respective sub-team mentor. -Within the first three weeks of the semester students will work with their -sub-team mentor to define midterm and -semester goals for their specific tasks within their sub-team. 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 @@ -627,7 +608,7 @@

    Teamwork and Interaction

    Course Format#

    -

    The course will by offered in a hybrid format. We have multiple online MS students who work in the course, so it is important to provide a mechanism for remote participation. +

    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.

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