Big Data and Quantum Mechanics

2017 ~ Present | Citrine Informatics

Goals

**Limited Admissions for the Spring 2021 Semester**

Leverage advances in machine learning and data analytics to enable faster and more accurate calculations of chemical properties using quantum-mechanical techniques such as density functional theory (DFT).

Students will learn about applications of quantum-mechanical simulations in photocatalysis and electrocatalysis, with particular focus on nitrogen fixation and water oxidation. These reactions have the potential to help alleviate global hunger by enabling distributed fertilizer production and helping to provide clean energy based on hydrogen fuel derived from water.

Issues Involved or Addressed

Simulation of molecular and surface systems using DFT calculations on high-performance computing resources; Training and testing of neural network models designed to predict energies of molecular and surface structures; Software development of tools for training, testing, and visualizing machine learning models for molecular systems. 
 
Note: This course does not involve any quantum computing or development of quantum-mechanical theories.

Methods and Technologies

  • Density Functional Theory
  • Databases
  • Automation
  • Neural Networks
  • Bayesian Statistics
  • High-performance Computing
  • Visualization
  • User Interface Design

Academic Majors of Interest

  • Computational Science and Engineering
  • OMSCS synchronous, case-by-case
  • Chemical and Biomolecular Engineering
  • Computer Engineering
  • Materials Science and Engineering
  • Chemistry
  • Mathematics
  • Physics
  • Statistics

Preferred Interests and Preparation

ChBE, Chem, MSE, Physics – Background/interest in quantum mechanics, computational materials science, computational chemistry. Curiosity about machine learning, data science, and big data. Programming skills would be helpful but are not required.

CS, Applied Math, Stats, CSE – Background/interest in machine learning, uncertainty quantification, data-driven methods. Interest in the intersection of machine learning and physics. Programming skills encouraged but not required.

CS – Interest in software architecture, database design, schema-free data models, functional programming, interactive visualization. Programming experience and skills are strongly encouraged.

Meeting Schedule & Location

Time 
9:30-10:20
Meeting Location 
ES&T L1 118
Meeting Day 
Wednesday

Team Advisors

Ms. Jagriti Sahoo
  • Chemical and Biomolecular Engineering
Dr. Andrew Medford
  • Chemical and Biomolecular Engineering

Partner(s) and Sponsor(s)

Citrine Informatics