Computational chemistry now plays a crucial role in both the design and the analysis of molecules and systems across industries including pharmaceuticals, materials, and manufacturing. This course will provide students with a conceptual understanding of computational modeling techniques pertinent to chemistry along with practical experience applying these methods. Specific techniques considered in the course may include quantum mechanical ab initio and semiempirical models, molecular mechanics, molecular dynamics simulations, optimization and sampling frameworks, and machine learning, with case studies coming from current literature. Emphasis will be placed on the trade-offs between model accuracy and efficiency, and fundamental principles in computer programming, numerical methods, hardware, and software will be introduced as they relate to this trade-off. Application of these methods to solve problems in diverse areas, such as protein structure, drug design, organic reactivity, and inorganic systems, will also be emphasized. In addition to regular computer-based exercises, the course will culminate in an independent project utilizing techniques presented in the course.

Units: 1

Max Enrollment: 12

Prerequisites: (CHEM 105 and CHEM 205) or (CHEM 116 and CHEM 205) or CHEM 120, and CHEM 211 and MATH 116, or permission of the instructor.

Instructor: Mavros

Distribution Requirements: MM - Mathematical Modeling and Problem Solving; NPS - Natural and Physical Sciences

Typical Periods Offered: Every other year; Fall

Semesters Offered this Academic Year: Fall

Notes: