Soft Materials Curriculum


To earn the Soft Matter specialization, students must complete the three courses designated as core courses of the SM program, one of the two laboratory courses in the SM program, and one elective course. These courses introduce students to soft materials, statistical mechanics, biophysics, and a variety of experimental techniques.

Core Courses:

PHYS 105a Biological Physics
Physical forces in living matter are studied from the perspective offered by statistical mechanics, elasticity theory, and fluid dynamics. Quantitative models for biological structure and function are developed and used to discuss recent experiments in single-molecule biology.

PHYS 163a Statistical Physics and Thermodynamics
The thermal properties of matter. Derivation of thermodynamics from statistical physics. Statistical theory of fluctuations.

PHYS 163b Principles of Soft Materials
Introduction to non-equilibrium statistical mechanics and applications of equilibrium and non-equilibrium statistical mechanics to understanding emergent phenomena in soft materials such as colloids, polymers and liquid crystals.

Lab courses:

QBIO 120b Quantitative Biology Instrumentation Laboratory
Focuses on optical and other instruments commonly used in biomedical laboratories to make quantitative measurements in vivo and in vitro. Students disassemble and reconfigure modular instruments in laboratory exercises that critically evaluate instrument reliability and usability and investigate the origins of noise and systematic error in measurements.

PHYS 169b Advanced Laboratory
Experiments in a range of topics in physics, possibly including selections from the following: wave optics, light scattering, Nuclear Magnetic Resonance, numerical simulation and modeling, phase transitions, laser tweezers, chaotic dynamics, and optical microscopy. Students work in depth on three or four experiments during the term.

Elective Courses:

PHYS 104a Condensed Matter Physics
Mechanical, thermal, and electronic properties of matter including fluids, solids, liquid crystals, and polymers. Simple models of matter are developed and used to discuss recent experimental findings.

QBIO 110a Numerical Modeling of Biological Systems
Modern scientific computation applied to problems in molecular and cell biology. Covers techniques such as numerical integration of differential equations, molecular dynamics and Monte Carlo simulations. Applications range from enzymes and molecular motors to cells.

COSI 178a Computational Molecular Biology
Information and computing technologies are becoming indispensable to modern biological research due to significant advances of high-throughput experimental technologies in recent years. This course presents an overview of the systemic development and application of computing systems and computational algorithms/techniques to the analysis of biological data, such as sequences, gene expression, protein expression, and biological networks. Hands-on training will be provided.

NBIO 136b Computational Neuroscience
An introduction to concepts and methods in computer modeling of neural systems. Topics include single and multicompartmental models of neurons, information representation and processing by populations of neurons, synaptic plasticity and models of learning, working memory, decision making and neural oscillations. Introductory tutorials in computer coding in Matlab will be featured throughout the course.

BCHM 102a Quantitative Approaches to Biochemical Systems
Introduces quantitative approaches to analyzing macromolecular structure and function. Emphasizes the use of basic thermodynamics and single-molecule and ensemble kinetics to elucidate biochemical reaction mechanisms. Also discusses the physical bases of spectroscopic and diffraction methods commonly used in the study of proteins and nucleic acids.

NPHY 115a Dynamical Systems
Covers analytic, computational and graphical methods for solving systems of coupled nonlinear ordinary differential equations. We study bifurcations, limit cycles, coupled oscillators and noise, with examples from physics, chemistry, population biology and many models of neurons.