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Materials structure, bonding and atomic arrangements leading to their properties and applications. Topics include electronic, thermal and mechanical behavior; emphasizing energy related materials and challenges.

Independent study in materials science under supervision of a faculty member.

Visualization is a powerful tool for communicating ideas and an indispensable skill for any scientist and engineer to master. Students will learn the cognitive science principles behind effective visualizations and practical tools for designing plots, schematics, infographics, etc. in STEM. They will critically assess the goals, biases, and efficacy of visuals, which in turn will bolster their understanding of the presented content. The course covers programmatic data visualization, digital illustrations, and interactive/animated graphics, which will be taught through lectures, discussions, hands-on sessions, and readings. Students will demonstrate mastery of course learning objectives through open-ended, creative projects. Examples will be drawn from materials science and related engineering disciplines. Suggested prerequisites: Introductory Python (e.g., CS 106A), introductory materials science (e.g., ENGR 50), and enthusiasm for visual representations of STEM concepts.

Understand the thermodynamics and efficiency limits of modern green technologies such as carbon dioxide capture from air, fuel cells, batteries, and geothermal power. Recommended: ENGR 50 or equivalent introductory materials science course. (Formerly 154)

Participation in a research project.

Experimental x-ray diffraction techniques for microstructural analysis of materials, emphasizing powder and single-crystal techniques. Diffraction from epitaxial and polycrystalline thin films, multilayers, and amorphorous materials using medium and high resolution configurations. Determination of phase purity, crystallinity, relaxation, stress, and texture in the materials. Advanced experimental x-ray diffraction techniques: reciprocal lattice mapping, reflectivity, and grazing incidence diffraction. Enrollment limited to 20. Undergraduates register for 162 for 4 units; graduates register for 172 for 3 units. Prerequisites: MATSCI 143 or equivalent course in materials characterization.Corequisites: MATSCI131 (Contact the instructor if you would like to enroll without completion of the stated prerequisites. A permission code will be provided with instructor approval)

This course introduces students to experimental techniques widely used in both industry and academia to characterize the mechanical properties of engineering materials. Students will learn how to perform tensile testing and nanoindentation experiments and how they can be used to study the mechanical behavior of several materials including metals, ceramics, and polymers. Through our laboratory sessions, students will also explore concepts related to materials fabrication and design, data analysis, performance optimization, and experimental decision-making. Enrollment is limited to 20. Prerequisites: MATSCI 151 or consent of instructor. Undergraduates register for 163 for 4 units, Graduates register for 173 for 3 units. Corequisites: MATSCI131 (Contact the instructor if you would like to enroll without completion of the stated prerequisites. A permission code will be provided with instructor approval)

Application of Data Science, Statistical Learning, and Machine Learning approaches to modern problems in Chemical and Materials Engineering. This course develops data science approaches, including their foundational mathematical and statistical basis, and applies these methods to data sets of limited size and precision. Methods for regression and clustering will be developed and applied, with an emphasis on validation and error quantification. Techniques that will be developed include linear and nonlinear regression, clustering and logistic regression, dimensionality reduction, unsupervised learning, neural networks, and hidden Markov models. These methods will be applied to a range of engineering problems, including conducting polymers, water purification membranes, battery materials, disease outcome prediction, genomic analysis, organic synthesis, and quality control in manufacturing. Prerequisites: CS 106A or permission from instructor. Undergraduates should enroll in 4 units and Graduates should enroll in 3 units. Corequisites: MATSCI131 (Contact the instructor if you would like to enroll without completion of the stated prerequisites. A permission code will be provided with instructor approval)