Department of Materials Science and Engineering

DSCI 330. Cognition and Computation. 3 Units.

An introduction to (1) theories of the relationship between cognition and computation; (2) computational models of human cognition (e.g. models of decision-making or concept creation); and (3) computational tools for the study of human cognition. All three dimensions involve AI and data science: theories compare natural and artificial intelligence and are tested against archives of data from brain imagining to linguistic corpora; models are derived from and tested against datasets of e.g., financial decisions (markets), legal rulings and findings (juries, judges, courts), legislative actions, and healthcare decisions, and moreover are often constructed via AI; computational tools aggregate data and operate upon it analytically, for search, recognition, tagging, machine learning, statistical description, and hypothesis testing, employing the full range of computational powers. Offered as COGS 330, COGS 430, DSCI 330 and DSCI 430.

DSCI 332. Geospatial Data Science: Explore, Analyze and Model Spatial, Temporal & Spatiotemporal Data. 3 Units.

This course on Geospatial Data Science focuses on leveraging Data Science Analytical Tools and Open Source Software to explore, process, integrate, analyze, visualize and model geospatial data targeting diverse applications. Students will learn the basic tools in R based code for Exploratory Data Analysis, Data Integration, Visualization of Geospatial Data and Data Modeling. QGIS will also be introduced in this course as an additional open-source tool for data visualization and manipulation. Students will work on geospatial datasets (air quality, water contamination, soil analysis, rocks properties, energy, transportation, public health, agriculture etc) developing entire data science pipelines from data collection to modeling(spatial, temporal or spatiotemporal). Students will be expected to learn the navigation of R Studio and develop data science pipelines using geospatial data. We include Vertex AI integration with Markov Cluster that enables students to leverage LLMs as code assistants. Students will be expected to learn the above and develop a 10 week modeling project focused on the use of spatial modeling methods with R using data relevant to their specific discipline or interest. Resulting scripts will be placed in a git repository for use by other students as open source resources along with documentation demonstrating the reproducible spatial modeling science and analyses for these problems. Examples of graduate projects from previous classes include subsurface modeling (geology), air quality monitoring, effects of air temperature on health and mortality rates(environment/health), earthquake mapping (geophysics/civil engineering), soil stability modeling (civil engineering), aquifer characterization (hydrology), pollution/contaminant mapping (environmental studies/medicine), water quality, crop growth(agriculture), predicting health outcomes integration with socioeconomic and environmental data. Offered as DSCI 332 and DSCI 432.

DSCI 351. Exploratory Data Science. 3 Units.

In this course, we will learn data science and analysis approaches to identify statistically significance relationships and better model and predict the behavior of these systems. We will assemble and explore real-world datasets, perform clustering and pair plot analyses to investigate correlations, and logistic regression will be employed to develop associated predictive models. Results will be interpreted, visualized and discussed. We will introduce basic elements of statistical analysis using R Project open source software for exploratory data analysis and model development. R is an open-source software project with broad abilities to access machine-readable open-data resources, data cleaning and munging functions, and a rich selection of statistical packages, used for data analytics, model development and prediction. This will include an introduction to R data types, reading and writing data, looping, plotting and regular expressions, so that one can start performing variable transformations for linear fitting and developing structural equation models, while exploring for statistically significant relationships. The M section of DSCI 351 is for students focusing on Materials Data Science. Offered as DSCI 351, DSCI 351M and DSCI 451. Prereq: (ENGR 130 or ENGR 131 or CSDS 132 or ECSE 132 or DSCI 134) and (STAT 312R or STAT 201R or SYBB 310 or PQHS 431).

DSCI 351M. Exploratory Data Science. 3 Units.

In this course, we will learn data science and analysis approaches to identify statistically significance relationships and better model and predict the behavior of these systems. We will assemble and explore real-world datasets, perform clustering and pair plot analyses to investigate correlations, and logistic regression will be employed to develop associated predictive models. Results will be interpreted, visualized and discussed. We will introduce basic elements of statistical analysis using R Project open source software for exploratory data analysis and model development. R is an open-source software project with broad abilities to access machine-readable open-data resources, data cleaning and munging functions, and a rich selection of statistical packages, used for data analytics, model development and prediction. This will include an introduction to R data types, reading and writing data, looping, plotting and regular expressions, so that one can start performing variable transformations for linear fitting and developing structural equation models, while exploring for statistically significant relationships. The M section of DSCI 351 is for students focusing on Materials Data Science. Offered as DSCI 351, DSCI 351M and DSCI 451. Prereq: (ENGR 130 or ENGR 131 or CSDS 132 or ECSE 132 or DSCI 134) and (STAT 312R or STAT 201R or SYBB 310 or PQHS 431).

DSCI 352. Applied Data Science Research. 3 Units.

This is a project based data science research class, in which project teams identify a research project under the guidance of a domain expert professor. For enrollment, students should provide an abstract of their research plan and data science question that they will address in the course as well as potential datasets that will be used. The research is structured as a data analysis project including the 6 steps of developing a reproducible data science project, including 1: Define the ADS question, 2: Identify, locate, and/or generate the data 3: Exploratory data analysis 4: Statistical modeling and prediction 5: Synthesizing the results in the domain context 6: Creation of reproducible research, Including code, datasets, documentation and reports. During the course special topic lectures will include Ethics, Privacy, Openness, Security, Ethics. Value. The M section of DSCI 352 is for students focusing on Materials Data Science. Offered as DSCI 352, DSCI 352M and DSCI 452. Prereq: DSCI 351 or DSCI 351M or DSCI 451.

DSCI 352M. Applied Data Science Research. 3 Units.

This is a project based data science research class, in which project teams identify a research project under the guidance of a domain expert professor. For enrollment, students should provide an abstract of their research plan and data science question that they will address in the course as well as potential datasets that will be used. The research is structured as a data analysis project including the 6 steps of developing a reproducible data science project, including 1: Define the ADS question, 2: Identify, locate, and/or generate the data 3: Exploratory data analysis 4: Statistical modeling and prediction 5: Synthesizing the results in the domain context 6: Creation of reproducible research, Including code, datasets, documentation and reports. During the course special topic lectures will include Ethics, Privacy, Openness, Security, Ethics. Value. The M section of DSCI 352 is for students focusing on Materials Data Science. Offered as DSCI 352, DSCI 352M and DSCI 452. Prereq: DSCI 351 or DSCI 351M or DSCI 451.

DSCI 353. Statistical and Machine Learning for Inference, Prediction and Reasoning. 3 Units.

In this course, we will use an open data science tool chain to develop reproducible data analyses useful for statistical and machine learning modeling for inference, prediction and reasoning on the behavior of complex systems. In addition to the standard data cleaning, assembly and exploratory data analysis steps essential to all data analyses, we will identify statistically significant relationships from datasets derived from population samples, and infer the reliability of these findings. We will use regression methods to model a number of both real-world and lab-based systems producing predictive models applicable in comparable populations. We will assemble and explore real-world datasets, perform clustering, self-similarity, and dimension reduction and linear and logistic regression to develop both fixed-effect and mixed-effect predictive models. We will introduce machine-learning approaches for classification and tree-based methods. We will use deep learning methods such as TensorFlow and PyTorch to develop neural network models of complex systems. Results will be interpreted, visualized and discussed. We will introduce the basic elements of data science and analytics using R Project open source software. R is an open-source software project with broad abilities to access machine-readable open-data resources, data cleaning and assembly functions, and a rich selection of statistical and deep learning packages, used for data analytics, model development, inference prediction and reasoning. With this background, it becomes possible to train linear regression, structural equation, fixed-effects and mixed-effects models along with other machine and deep learning models, while exploring statistically significant relationships. The class will be structured to have a balance of theory and practice. We split class sessions into Foundation and Practicum a) Foundation: lectures, presentations, discussion b) Practicum: coding, demonstrations and hands-on data science work. The M section of DSCI 353 is for students focusing on Materials Data Science. Offered as DSCI 353, DSCI 353M and DSCI 453. Prereq: DSCI 351 or DSCI 351M.

DSCI 353M. Statistical and Machine Learning for Inference, Prediction and Reasoning. 3 Units.

In this course, we will use an open data science tool chain to develop reproducible data analyses useful for statistical and machine learning modeling for inference, prediction and reasoning on the behavior of complex systems. In addition to the standard data cleaning, assembly and exploratory data analysis steps essential to all data analyses, we will identify statistically significant relationships from datasets derived from population samples, and infer the reliability of these findings. We will use regression methods to model a number of both real-world and lab-based systems producing predictive models applicable in comparable populations. We will assemble and explore real-world datasets, perform clustering, self-similarity, and dimension reduction and linear and logistic regression to develop both fixed-effect and mixed-effect predictive models. We will introduce machine-learning approaches for classification and tree-based methods. We will use deep learning methods such as TensorFlow and PyTorch to develop neural network models of complex systems. Results will be interpreted, visualized and discussed. We will introduce the basic elements of data science and analytics using R Project open source software. R is an open-source software project with broad abilities to access machine-readable open-data resources, data cleaning and assembly functions, and a rich selection of statistical and deep learning packages, used for data analytics, model development, inference prediction and reasoning. With this background, it becomes possible to train linear regression, structural equation, fixed-effects and mixed-effects models along with other machine and deep learning models, while exploring statistically significant relationships. The class will be structured to have a balance of theory and practice. We split class sessions into Foundation and Practicum a) Foundation: lectures, presentations, discussion b) Practicum: coding, demonstrations and hands-on data science work. The M section of DSCI 353 is for students focusing on Materials Data Science. Offered as DSCI 353, DSCI 353M and DSCI 453. Prereq: DSCI 351 or DSCI 351M.

DSCI 354. Data Visualization and Analytics. 3 Units.

This course explores advanced techniques for visualizing and analyzing complex datasets, including point-in time, time-series, spectral, and image data. Students will enhance their exploratory data analysis (EDA) and data cleaning workflows to transform raw information into insights for communication and decision making. A component of this course will focus on creating interactive visualizations (e.g., dynamic plots, Shiny applications, and 3D models). The goal of the course is to develop data visualizations that are tailored for diverse audiences. A mixed reality component is included in this course so students learn to develop visualizations within an immersive environment. Beyond technical skills, the course examines the ethics of data representation and the theory of how audiences interpret information. This course uses a Git repository, open-source resources, and reproducible data science tooling. Offered as DSCI 354, DSCI 354M, and DSCI 454. Prereq: (DSCI 351 or DSCI 351M) and (DSCI 353 or DSCI 353M).

DSCI 354M. Data Visualization and Analytics. 3 Units.

This course explores advanced techniques for visualizing and analyzing complex datasets, including point-in time, time-series, spectral, and image data. Students will enhance their exploratory data analysis (EDA) and data cleaning workflows to transform raw information into insights for communication and decision making. A component of this course will focus on creating interactive visualizations (e.g., dynamic plots, Shiny applications, and 3D models). The goal of the course is to develop data visualizations that are tailored for diverse audiences. A mixed reality component is included in this course so students learn to develop visualizations within an immersive environment. Beyond technical skills, the course examines the ethics of data representation and the theory of how audiences interpret information. This course uses a Git repository, open-source resources, and reproducible data science tooling. Offered as DSCI 354, DSCI 354M, and DSCI 454. Prereq: (DSCI 351 or DSCI 351M) and (DSCI 353 or DSCI 353M).

DSCI 355. Applied Data Science (ADS) Tooling: Data Management, Open Source Packages and Infrastructure. 3 Units.

This is an introductory course to provide practical knowledge and resources in Applied Data Sciences(ADS) Tools that can be applied to different areas where code is developed for data analysis and modeling. This course focuses on practical aspects of Applied Data Science to complement the traditional ADS curriculum. When new code, pipelines, algorithms and models are developed, they need to be implemented, scalable, reusable, understandable and efficient. Most of those aspects are not traditionally covered in core ADS classes. This course proposes to fill the gap in some important areas which includes creating and publishing code packages and R and Python, agile software development, coding good practices and documentation, version control (git), Data Management, foundations of infrastructure and LLMs as code assistants. This course represents an opportunity for students to learn useful applied data science tools to boost their careers and provide practical experience in developing data science solutions and applications. Graduate students will work on a hands-on open source project which could cover different aspects of the course depending on their interest. One example is making a R/Python package based on their research or developing an automated data analysis pipeline with efficient and well documented code. ADS Tooling is an introductory course that provides foundational and practical concepts and implementations of data science technologies useful to any domain, and therefore the course is open to students in any school. Students should have experience in R or Python to enroll in this course. Offered as DSCI 355 and DSCI 455. Prereq: (DSCI 351 or DSCI 351M or DSCI 451) and (DSCI 353 or DSCI 353M or DSCI 453) or Requisites Not Met permission.

DSCI 430. Cognition and Computation. 3 Units.

An introduction to (1) theories of the relationship between cognition and computation; (2) computational models of human cognition (e.g. models of decision-making or concept creation); and (3) computational tools for the study of human cognition. All three dimensions involve AI and data science: theories compare natural and artificial intelligence and are tested against archives of data from brain imagining to linguistic corpora; models are derived from and tested against datasets of e.g., financial decisions (markets), legal rulings and findings (juries, judges, courts), legislative actions, and healthcare decisions, and moreover are often constructed via AI; computational tools aggregate data and operate upon it analytically, for search, recognition, tagging, machine learning, statistical description, and hypothesis testing, employing the full range of computational powers. Offered as COGS 330, COGS 430, DSCI 330 and DSCI 430.

DSCI 432. Geospatial Data Science: Explore, Analyze and Model Spatial, Temporal & Spatiotemporal Data. 3 Units.

This course on Geospatial Data Science focuses on leveraging Data Science Analytical Tools and Open Source Software to explore, process, integrate, analyze, visualize and model geospatial data targeting diverse applications. Students will learn the basic tools in R based code for Exploratory Data Analysis, Data Integration, Visualization of Geospatial Data and Data Modeling. QGIS will also be introduced in this course as an additional open-source tool for data visualization and manipulation. Students will work on geospatial datasets (air quality, water contamination, soil analysis, rocks properties, energy, transportation, public health, agriculture etc) developing entire data science pipelines from data collection to modeling(spatial, temporal or spatiotemporal). Students will be expected to learn the navigation of R Studio and develop data science pipelines using geospatial data. We include Vertex AI integration with Markov Cluster that enables students to leverage LLMs as code assistants. Students will be expected to learn the above and develop a 10 week modeling project focused on the use of spatial modeling methods with R using data relevant to their specific discipline or interest. Resulting scripts will be placed in a git repository for use by other students as open source resources along with documentation demonstrating the reproducible spatial modeling science and analyses for these problems. Examples of graduate projects from previous classes include subsurface modeling (geology), air quality monitoring, effects of air temperature on health and mortality rates(environment/health), earthquake mapping (geophysics/civil engineering), soil stability modeling (civil engineering), aquifer characterization (hydrology), pollution/contaminant mapping (environmental studies/medicine), water quality, crop growth(agriculture), predicting health outcomes integration with socioeconomic and environmental data. Offered as DSCI 332 and DSCI 432.

DSCI 451. Exploratory Data Science. 3 Units.

In this course, we will learn data science and analysis approaches to identify statistically significance relationships and better model and predict the behavior of these systems. We will assemble and explore real-world datasets, perform clustering and pair plot analyses to investigate correlations, and logistic regression will be employed to develop associated predictive models. Results will be interpreted, visualized and discussed. We will introduce basic elements of statistical analysis using R Project open source software for exploratory data analysis and model development. R is an open-source software project with broad abilities to access machine-readable open-data resources, data cleaning and munging functions, and a rich selection of statistical packages, used for data analytics, model development and prediction. This will include an introduction to R data types, reading and writing data, looping, plotting and regular expressions, so that one can start performing variable transformations for linear fitting and developing structural equation models, while exploring for statistically significant relationships. The M section of DSCI 351 is for students focusing on Materials Data Science. Offered as DSCI 351, DSCI 351M and DSCI 451.

DSCI 452. Applied Data Science Research. 3 Units.

This is a project based data science research class, in which project teams identify a research project under the guidance of a domain expert professor. For enrollment, students should provide an abstract of their research plan and data science question that they will address in the course as well as potential datasets that will be used. The research is structured as a data analysis project including the 6 steps of developing a reproducible data science project, including 1: Define the ADS question, 2: Identify, locate, and/or generate the data 3: Exploratory data analysis 4: Statistical modeling and prediction 5: Synthesizing the results in the domain context 6: Creation of reproducible research, Including code, datasets, documentation and reports. During the course special topic lectures will include Ethics, Privacy, Openness, Security, Ethics. Value. The M section of DSCI 352 is for students focusing on Materials Data Science. Offered as DSCI 352, DSCI 352M and DSCI 452.

DSCI 453. Statistical and Machine Learning for Inference, Prediction and Reasoning. 3 Units.

In this course, we will use an open data science tool chain to develop reproducible data analyses useful for statistical and machine learning modeling for inference, prediction and reasoning on the behavior of complex systems. In addition to the standard data cleaning, assembly and exploratory data analysis steps essential to all data analyses, we will identify statistically significant relationships from datasets derived from population samples, and infer the reliability of these findings. We will use regression methods to model a number of both real-world and lab-based systems producing predictive models applicable in comparable populations. We will assemble and explore real-world datasets, perform clustering, self-similarity, and dimension reduction and linear and logistic regression to develop both fixed-effect and mixed-effect predictive models. We will introduce machine-learning approaches for classification and tree-based methods. We will use deep learning methods such as TensorFlow and PyTorch to develop neural network models of complex systems. Results will be interpreted, visualized and discussed. We will introduce the basic elements of data science and analytics using R Project open source software. R is an open-source software project with broad abilities to access machine-readable open-data resources, data cleaning and assembly functions, and a rich selection of statistical and deep learning packages, used for data analytics, model development, inference prediction and reasoning. With this background, it becomes possible to train linear regression, structural equation, fixed-effects and mixed-effects models along with other machine and deep learning models, while exploring statistically significant relationships. The class will be structured to have a balance of theory and practice. We split class sessions into Foundation and Practicum a) Foundation: lectures, presentations, discussion b) Practicum: coding, demonstrations and hands-on data science work. The M section of DSCI 353 is for students focusing on Materials Data Science. Offered as DSCI 353, DSCI 353M and DSCI 453.

DSCI 454. Data Visualization and Analytics. 3 Units.

This course explores advanced techniques for visualizing and analyzing complex datasets, including point-in time, time-series, spectral, and image data. Students will enhance their exploratory data analysis (EDA) and data cleaning workflows to transform raw information into insights for communication and decision making. A component of this course will focus on creating interactive visualizations (e.g., dynamic plots, Shiny applications, and 3D models). The goal of the course is to develop data visualizations that are tailored for diverse audiences. A mixed reality component is included in this course so students learn to develop visualizations within an immersive environment. Beyond technical skills, the course examines the ethics of data representation and the theory of how audiences interpret information. This course uses a Git repository, open-source resources, and reproducible data science tooling. Offered as DSCI 354, DSCI 354M, and DSCI 454. Prereq: DSCI 451 and DSCI 453.

DSCI 455. Applied Data Science (ADS) Tooling: Data Management, Open Source Packages and Infrastructure. 3 Units.

This is an introductory course to provide practical knowledge and resources in Applied Data Sciences(ADS) Tools that can be applied to different areas where code is developed for data analysis and modeling. This course focuses on practical aspects of Applied Data Science to complement the traditional ADS curriculum. When new code, pipelines, algorithms and models are developed, they need to be implemented, scalable, reusable, understandable and efficient. Most of those aspects are not traditionally covered in core ADS classes. This course proposes to fill the gap in some important areas which includes creating and publishing code packages and R and Python, agile software development, coding good practices and documentation, version control (git), Data Management, foundations of infrastructure and LLMs as code assistants. This course represents an opportunity for students to learn useful applied data science tools to boost their careers and provide practical experience in developing data science solutions and applications. Graduate students will work on a hands-on open source project which could cover different aspects of the course depending on their interest. One example is making a R/Python package based on their research or developing an automated data analysis pipeline with efficient and well documented code. ADS Tooling is an introductory course that provides foundational and practical concepts and implementations of data science technologies useful to any domain, and therefore the course is open to students in any school. Students should have experience in R or Python to enroll in this course. Offered as DSCI 355 and DSCI 455. Prereq: (DSCI 351 or DSCI 351M or DSCI 451) and (DSCI 353 or DSCI 353M or DSCI 453) or Requisites Not Met permission.

EMSE 102. Materials for Current and Future Technologies. 1 Unit.

Open to all students discussing the importance of materials on current and future technologies. The course will be a series of seminars by the faculty at the Department of Materials Science and Engineering covering important topics such as materials processing, use of materials in a variety of technologically important areas; e.g., construction, energy related technologies, biomedical applications and space applications.

EMSE 110. Transitioning Ideas to Reality I - Materials in Service of Industry and Society. 1 Unit.

In order for ideas to impact the lives of individuals and society they must be moved from "blue sky" to that which is manufacturable. Therein lies true creativity - design under constraint. Greater Cleveland is fortunate to have a diverse set of industries that serve medical, aerospace, electric, and advanced-materials technologies. This course involves trips to an array of work sites of leading companies to witness first-hand the processes and products, and to interact directly with practitioners. Occasional in-class speakers with demonstrations will be used when it is not logistically reasonable to visit off-site.

EMSE 120. Transitioning Ideas to Reality II - Manufacturing Laboratory. 2 Units.

This course complements EMSE 110. In that class students witness a diverse array of processing on-site in industry. In this class students work in teams and as individuals within processing laboratories working with an array of "real materials" to explore the potential of casting, machining, and deformation processes to produce real parts and/or components. An introduction to CAD as a means of communication is provided. The bulk of the term is spent in labs doing hands-on work. Planned work is carried out to demonstrate techniques and potential. Students have the opportunity to work independently or in teams to produce articles as varied as jewelry, electronics, transportation vehicles, or novel components or devices of the students' choosing.

EMSE 125. First Year Research in Materials Science and Engineering. 1 Unit.

First year students conduct independent research in the area of material science and engineering, working closely with graduate student(s) and/or postdoctoral fellow(s), and supervised by an EMSE faculty member. An average of 5-6 hr/wk in the laboratory, periodic updates, and an end of semester report is required. Prereq: Limited to first-year undergraduate students.

EMSE 220. Materials Laboratory I. 2 Units.

Experiments designed to introduce processing, microstructure and property relationships of metal alloys and ceramics. Solidification of a binary alloy and metallography by optical and scanning electron microscopy. Synthesis of ceramics powders, thermal analysis using thermogravimetric analysis and differential thermal analysis, powder consolidation. Kinetics of high-temperature sintering, grain growth, and metal oxidation. Statistical analysis of experimental results. Recommended preparation or recommended co-requisite: EMSE 276. This course satisfies the GER Disciplinary Communication requirement only in combination with EMSE 320. Counts as a Disciplinary Communication course.

EMSE 228. Mathematical and Computational Methods for Materials Science and Engineering. 3 Units.

The course combines fundamental topics of material science and engineering with underlying mathematical methods and coding for computation. Focusing on the mathematics of vectors and using Mathematica as computational framework, the course teaches how to solve problems drawn from crystallography, diffraction, imaging of materials, and image processing. Students will develop a fundamental understanding of the basis for solving these problems including understanding the constituent equations, solution methods, and analysis and presentation of results. Prereq: (ENGR 130 or ENGR 131 or CSDS 132 or ECSE 132) and (ENGR 145 or CHEM 106 or PHYS 221).

EMSE 276. Materials Properties: Composition and Structure. 3 Units.

Relation of crystal structure, microstructure, and chemical composition to the selected properties of materials. The role materials processing has in controlling structure so as to obtain desired properties, using examples from metals, semiconductors, ceramics, and composites. Demonstration of properties determined by type and strength of interatomic bonding; presence of crystallographic defects; and microstructural control. Use of diagrams, frameworks, and mathematical relationships to evaluate and predict properties. A systematic review of characterization methods for assessing the state of different classes of materials will be provided throughout the course. Prereq: MATH 121 and (CHEM 106 or PHYS 221 or ENGR 145). Prereq or Coreq: PHYS 122 or PHYS 124.

EMSE 305. Phase Transformations in Materials. 3 Units.

This course covers the fundamentals of phase transformations with specific focus on thermodynamic driving forces, nucleation and growth kinetics, and microstructure evolution. Major sections on solidification, diffusional transformations (e.g. precipitation, cellular, eutectoidal, spinodal decomposition, etc.) and non-diffusional transformations (e.g. martensitic, magnetic, ferroelectric, etc.) will be introduced. Prereq: EMSE 276.

EMSE 308. Welding Metallurgy. 3 Units.

Introduction to arc welding and metallurgy of welding. The course provides a broad overview of different industrial applications requiring welding, the variables controlling critical property requirements of the weld and a survey of the different types of arc welding processes. The course details the fundamental concepts that govern the different aspects of arc welding including the welding arc, weld pool solidification, precipitate formation and solid state phase transformations. Offered as EMSE 308 and EMSE 408. Coreq: EMSE 327.

EMSE 314. Electrical, Magnetic, Optical, and Thermal Properties of Materials. 3 Units.

Reviews quantum mechanics as applied to materials, energy bands, and density of states; Electrical properties of metals, semiconductors, insulators, and superconductors; Optical properties of materials, including: metallic luster, color, and optoelectronics; Magnetic properties of materials, including: Types of magnetic behavior, theory, and applications; Thermal properties of materials, including: heat capacity, thermal expansion, and thermal conductivity. Offered as EMSE 314 and EMSE 414. Prereq: EMSE 276 and (PHYS 122 or PHYS 124).

EMSE 319. Processing and Manufacturing of Materials. 3 Units.

Introduction to processing technologies by which materials are manufactured into engineering components. Discussion of how processing methods are dependent on desired composition, structure, microstructure, and defects, and how processing affects material performance. Emphasis will be placed on processes and treatments to achieve or improve chemical, mechanical, physical performance and/or aesthetics, including: casting, welding, forging, cold-forming, powder processing of metals and ceramics, and polymer and composite processing. Coverage of statistics and computational tools relevant to materials manufacturing. Prereq: EMSE 276.

EMSE 320. Materials Laboratory II. 1 Unit.

Introduce the design of microstructural characterization approaches to elucidate the structure and/or microstructure. It will include demonstrations of appropriate techniques for different material classes. The characterization is the enabler of the materials science and engineering triad: processing-structure-properties-performance. Students will reflect on how professional standards, statistical analysis, and instrument limitations require that engineers and scientists conduct failure analysis and interpret conclusions. Recommended preparation: EMSE 276. Recommended corequisite: EMSE 372. This course satisfies the GER Disciplinary Communication requirement only in combination with EMSE 220. Counts as a Disciplinary Communication course.

EMSE 325. Undergraduate Research in Materials Science and Engineering. 1 - 3 Units.

Undergraduate laboratory research in materials science and engineering. Students will undertake an independent research project alongside graduate student(s) and/or postdoctoral fellow(s), and will be supervised by an EMSE faculty member. Written and oral reports will be given on a regular basis, and an end of semester report is required. The course can be repeated up to four (4) times for a total of six (6) credit hours. Prereq: Sophomore or Junior standing and consent of instructor.

EMSE 327. Thermodynamic Stability and Rate Processes. 3 Units.

An introduction to thermodynamics of materials as applied to metals, ceramics, polymers and optical/radiant heat transfer for photovoltaics. The laws of thermodynamics are introduced and the general approaches used in the thermodynamic method are presented. Systems studied span phase stability and oxidation in metals and oxides; nitride ceramics and semiconductors; polymerization, crystallization and block copolymer domain formation; and the thermodynamics of systems such as for solar power collection and conversion. Recommended preparation: EMSE 228 and ENGR 225 or equivalent. Prereq: EMSE 276 or EMSE 201.

EMSE 328. Mesoscale Structural Control of Functional Materials. 3 Units.

The course focuses on mesoscale structure of materials and their interrelated effects on properties, mostly in electrical in nature. The mesoscale science covers the structures varying from electronic- to micro-structure. In each scale, fundamental science will be complimented by examples of applications and how the structure is exploited both to modify and enable function. The student will develop an understanding of how the structure across multiple scales are interrelated and how to tailor them for desired outcomes. Offered as EMSE 328 and EMSE 428. Prereq: (MATH 223 or MATH 227) and (ENGR 145 or CHEM 106 or PHYS 221).

EMSE 330. Materials Laboratory III. 2 Units.

Experiments designed to characterize and evaluate different microstructures produced by variations in processing. Hardenability of steels, TTT and CT diagrams, precipitation hardening of alloys and fracture of brittle materials. Statistical analysis of experimental results. Recommended preparation: EMSE 276. Recommended preparation or co-requisites: EMSE 327.

EMSE 335. Strategic Metals and Materials for the 21st Century. 3 Units.

This course seeks to create an understanding of the role of mineral-based materials in the modern economy focusing on how such knowledge can and should be used in making strategic choices in an engineering context. The history of the role of materials in emerging technologies from a historical perspective will be briefly explored. The current literature will be used to demonstrate the connectedness of materials availability and the development and sustainability of engineering advances with examples of applications exploiting structural, electronic, optical, magnetic, and energy conversion properties. Processing will be comprehensively reviewed from source through refinement through processing including property development through application of an illustrative set of engineering materials representing commodities, less common metals, and minor metals. The concept of strategic recycling, including design for recycling and waste stream management will be considered. Offered as EMSE 335 and EMSE 435. Prereq: Senior standing or graduate student.

EMSE 343. Processing of Electronic Materials. 3 Units.

The class will focus on the processing of materials for electronic applications. Necessary background into the fundamentals and applications will be given at the beginning to provide the basis for choices made during processing. MOSFET will be used as the target application. However, the processing steps covered are related to many other semiconductor based applications. The class will include both planar and bulk processing. Offered as: EMSE 343 and EMSE 443. Prereq: (PHYS 122 or PHYS 124) and EMSE 276.

EMSE 345. Engineered Materials for Biomedical Applications. 3 Units.

A survey of synthetic biomedical materials from the perspective of materials science and engineering, focusing on how processing/synthesis, structure, and properties determine materials performance under the engineering demands imposed by physiological environments. Comparisons and contrasts between engineered metals, ceramics, and polymers, versus the biological materials they are called on to replace; consequences for materials and device design. Biomedical materials in applications such as orthopedic implants, dental restorations, wound healing, ophthalmic materials, and biomedical microelectromechanical systems (bioMEMS). Additive manufacturing of biomedical materials. Prereq: ENGR 200 and ENGR 145.

EMSE 349. Role of Materials in Energy and Sustainability. 3 Units.

This course has two parts: engineered materials as consumers of resources (raw materials, energy); and as key contributors to energy efficiency and sustainable energy technologies. Topics covered include: Energy usage in the U.S. and the world. Availability of raw materials, including strategic materials; factors affecting global reserves and annual world production. Resource demand of materials production, fabrication, and recycling. Design strategies, and how the inclusion of environmental impacts in design criteria can affect design outcomes and material selection. Roles of engineered materials in energy technologies: photovoltaics, solar thermal, fuel cells, wind, batteries, capacitors. Materials in energy-efficient lighting. Energy return on energy invested. Semester projects will allow students to explore related topics (e.g. geothermal; biomass; energy-efficient manufacturing and transportation). Offered as EMSE 349 and EMSE 449. Prereq: (ENGR 225 or EMAE 251 or EMAC 351) and ENGR 145 and (PHYS 122 or PHYS 124) or Requisites Not Met permission.

EMSE 368. Thesis/Article Writing for Scientists and Engineers. 3 Units.

For writing a publication, such as a thesis or a journal article, in a field of science or engineering, students need a diverse set of skills in addition to mastering the scientific content. Generally, scientific writing requires proficiency in document organization, professional presentation of numerical and graphical data, literature retrieval and management, text processing, version control, graphical illustration, the English language, elements of style, etc. For scientists and engineers, specifically, writing a publication requires additional knowledge about e.g. conventions of numerical precision, error limits, mathematical typesetting, proper use of units, proper digital processing of micrographs, etc. Having to acquire these essential skills at the beginning of writing a thesis or a journal article may compromise the outcome by distracting from the most important task of composing the best possible scientific content. This course properly prepares students for scientific writing with a comprehensive spectrum of knowledge, skills, and tools enabling them to fully focus on the scientific content of their thesis or publication when the time has come to start writing. Similar to artistic drawing, where the ability to "see" is as (or more!) important as skills of the hand, the ability of proper scientific writing is intimately linked to the ability of critically reviewing scientific texts. Therefore, students will practice both authoring and critical reviewing of scientific texts. To sharpen students' skills of reviewing, examples of good and less good scientific writing will be taken from published literature in science and engineering and analyzed in the context of knowledge acquired in the course. At the end of the course, students will have set up skills and a highly functional work environment to start writing their role thesis or article with full focus on the scientific content. Students of all disciplines of science and engineering will benefit from the course material. Offered as EMSE 368 and EMSE 468.

EMSE 372. Structural Materials by Design. 4 Units.

Materials selection and design of mechanical and structural elements with respect to static failure, elastic stability, residual stresses, stress concentrations, impact, fatigue, creep, and environmental conditions. Mechanical behavior of engineering materials (metals, polymers, ceramics, composites). Influence of ultrastructural and microstructural aspects of materials on mechanical properties. Mechanical test methods covered. Models of deformation behavior of isotropic and anisotropic materials. Methods to analyze static and fatigue fracture properties. Rational approaches to materials selection for new and existing designs of structures. Failure analysis methods and examples, and the professional ethical responsibility of the engineer. Four mandatory laboratories, with reports. Offered as EMAE 372 and EMSE 372. Prereq: ENGR 200.

EMSE 379. Design for Lifetime Performance. 3 Units.

The roles of processing and properties of a material on its performance, cost, maintenance, degradation and end-of-life treatment. Corrosion and oxidation, hydrogen and transformation-induced degradation mechanisms. Defects from prior processing. New defects generated during use/operation, e.g. generated from radiation, stress, heat etc. Accumulation and growth of defects during use/operation; crack nucleation, propagation, failure. Impact, abrasion, wear, corrosion/oxidation/reduction, stress-corrosion, fatigue, fretting, creep. Evaluation of degradation: Non-destructive versus destructive methods, estimation of remaining lifetime. Mitigation of degradation mechanisms. Statistical tools for assessing a material's lifetime performance. Capstone design project. Prereq: EMSE 372. Coreq: EMSE 319.

EMSE 396. Special Project or Thesis. 1 - 18 Units.

Special research projects or undergraduate thesis in selected material areas.

EMSE 398. Senior Project in Materials I. 1 Unit.

Independent Research project. Projects selected from those suggested by faculty; usually entail original research. The EMSE 398 and 399 sequence form an approved SAGES capstone. Counts as a SAGES Senior Capstone course.

EMSE 399. Senior Project in Materials II. 2 Units.

Independent Research project. Projects selected from those suggested by faculty; usually entail original research. Requirements include periodic reporting of progress, plus a final oral presentation and written report. Counts as a SAGES Senior Capstone course. Prereq: EMSE 398.

EMSE 400T. Graduate Teaching I. 0 Unit.

To provide teaching experience for all Ph.D.-bound graduate students. This will include preparing exams/quizzes, homework, leading recitation sessions, tutoring, providing laboratory assistance, and developing teaching aids that include both web-based and classroom materials. Graduate students will meet with supervising faculty member throughout the semester. Grading is pass/fail. Students must receive three passing grades and up to two assignments may be taken concurrently. Recommended preparation: Ph.D. student in Materials Science and Engineering.

EMSE 408. Welding Metallurgy. 3 Units.

Introduction to arc welding and metallurgy of welding. The course provides a broad overview of different industrial applications requiring welding, the variables controlling critical property requirements of the weld and a survey of the different types of arc welding processes. The course details the fundamental concepts that govern the different aspects of arc welding including the welding arc, weld pool solidification, precipitate formation and solid state phase transformations. Offered as EMSE 308 and EMSE 408.

EMSE 409. Deformation Processing. 3 Units.

Flow stress as a function of material and processing parameters; yielding criteria; stress states in elastic-plastic deformation; forming methods: forging, rolling, extrusion, drawing, stretch forming, composite forming.

EMSE 413. Fundamentals of Materials Engineering and Science. 3 Units.

Provides a background in materials for graduate students with undergraduate majors in other branches of engineering and science: reviews basic bonding relations, structure, and defects in crystals. Lattice dynamics; thermodynamic relations in multi-component systems; microstructural control in metals and ceramics; mechanical and chemical properties of materials as affected by structure; control of properties by techniques involving structure property relations; basic electrical, magnetic and optical properties.

EMSE 414. Electrical, Magnetic, Optical, and Thermal Properties of Materials. 3 Units.

Reviews quantum mechanics as applied to materials, energy bands, and density of states; Electrical properties of metals, semiconductors, insulators, and superconductors; Optical properties of materials, including: metallic luster, color, and optoelectronics; Magnetic properties of materials, including: Types of magnetic behavior, theory, and applications; Thermal properties of materials, including: heat capacity, thermal expansion, and thermal conductivity. Offered as EMSE 314 and EMSE 414. Prereq: Graduate Standing in Materials Science and Engineering or Requisites Not Met permission.

EMSE 417. Properties of Materials in Extreme Environments. 3 Units.

Fundamentals of degradation pathways of materials under extreme conditions; thermodynamic stability of microstructures, deformation mechanisms, and failure mechanisms. Extreme conditions that will typically be addressed include: elevated temperatures, high-strain rates (ballistic), environmental effects, nuclear radiation, and small scales. Examples will be drawn from recent events as appropriate.

EMSE 421. Fracture of Materials. 3 Units.

Micromechanisms of deformation and fracture of engineering materials. Brittle fracture and ductile fracture mechanisms in relation to microstructure. Strength, toughness, and test techniques. Review of predictive models. Recommended preparation: ENGR 200 and EMSE 427; or consent.

EMSE 422. Failure Analysis. 3 Units.

Methods and procedures for determining the basic causes of failures in structures and components. Recognition of fractures and excessive deformations in terms of their nature and origin. Development and full characterization of fractures. Review of essential mechanical behavior concepts and fracture mechanics concepts applied to failure analyses in inorganic, organic, and composite systems. Legal, ethical, and professional aspects of failures from service. Prereq: EMSE 372 or EMAE 372 or Requisites Not Met permission.

EMSE 427. Defects in Solids. 3 Units.

Defects in solids control many properties of interest to the materials scientist or engineer. This course focuses on point, line, and interfacial defects in crystals and their interactions, including calculations of defect energies and interaction forces. Crystallographic defects presented include point defects (e.g., vacancies, interstitials, substitutional and interstitial impurities), line defects (e.g., dislocations), and planar defects (e.g., grain boundaries). The consequence of point defects on diffusion as well as on optical and electronic properties is discussed. Dislocation motion and dislocation dissociation are treated, and the influence of dislocation dynamics on yield phenomena, work hardening, and other mechanical properties are discussed. The role of grain boundaries and inter-phase boundaries in determining the physical properties of the material are presented. Experimental techniques for characterizing defects are integrated throughout the course. Recommended preparation: MATH 223 (or equivalent) and EMSE 276 (or equivalent).

EMSE 428. Mesoscale Structural Control of Functional Materials. 3 Units.

The course focuses on mesoscale structure of materials and their interrelated effects on properties, mostly in electrical in nature. The mesoscale science covers the structures varying from electronic- to micro-structure. In each scale, fundamental science will be complimented by examples of applications and how the structure is exploited both to modify and enable function. The student will develop an understanding of how the structure across multiple scales are interrelated and how to tailor them for desired outcomes. Offered as EMSE 328 and EMSE 428.

EMSE 435. Strategic Metals and Materials for the 21st Century. 3 Units.

This course seeks to create an understanding of the role of mineral-based materials in the modern economy focusing on how such knowledge can and should be used in making strategic choices in an engineering context. The history of the role of materials in emerging technologies from a historical perspective will be briefly explored. The current literature will be used to demonstrate the connectedness of materials availability and the development and sustainability of engineering advances with examples of applications exploiting structural, electronic, optical, magnetic, and energy conversion properties. Processing will be comprehensively reviewed from source through refinement through processing including property development through application of an illustrative set of engineering materials representing commodities, less common metals, and minor metals. The concept of strategic recycling, including design for recycling and waste stream management will be considered. Offered as EMSE 335 and EMSE 435. Prereq: Senior standing or graduate student.

EMSE 443. Processing of Electronic Materials. 3 Units.

The class will focus on the processing of materials for electronic applications. Necessary background into the fundamentals and applications will be given at the beginning to provide the basis for choices made during processing. MOSFET will be used as the target application. However, the processing steps covered are related to many other semiconductor based applications. The class will include both planar and bulk processing. Offered as: EMSE 343 and EMSE 443. Prereq: (PHYS 122 or PHYS 124) and EMSE 276.

EMSE 449. Role of Materials in Energy and Sustainability. 3 Units.

This course has two parts: engineered materials as consumers of resources (raw materials, energy); and as key contributors to energy efficiency and sustainable energy technologies. Topics covered include: Energy usage in the U.S. and the world. Availability of raw materials, including strategic materials; factors affecting global reserves and annual world production. Resource demand of materials production, fabrication, and recycling. Design strategies, and how the inclusion of environmental impacts in design criteria can affect design outcomes and material selection. Roles of engineered materials in energy technologies: photovoltaics, solar thermal, fuel cells, wind, batteries, capacitors. Materials in energy-efficient lighting. Energy return on energy invested. Semester projects will allow students to explore related topics (e.g. geothermal; biomass; energy-efficient manufacturing and transportation). Offered as EMSE 349 and EMSE 449. Prereq: ENGR 225 and (ENGR 145 or EMSE 146) and (PHYS 122 or PHYS 124) or requisites not met permission.

EMSE 463. Magnetism and Magnetic Materials. 3 Units.

This course covers the fundamentals of magnetism and application of modern magnetic materials especially for energy and data storage technologies. The course will focus on intrinsic and extrinsic magnetic properties, processing of magnetic materials to achieve important magnetic performance metrics, and the state-of-the-art magnetic materials used today. The topics related to intrinsic properties, include: magnetic dipole moments, magnetization, exchange coupling, magnetic anisotropy and magnetostriction. Topics related to extrinsic properties, include: magnetic hysteresis, frequency dependent magnetic response and magnetic losses. Technologically important permanent magnets (including rare earth containing alloys and magnetic oxides), soft magnets (including electrical steels, amorphous, ferrites, and nanocrystalline alloys), and thin film materials (including iron platinum) will be discussed in the context of their technological interest. Throughout the course, experimental techniques and data analysis will be discussed. The course is suitable for most graduate students and advanced undergraduates in engineering and science.

EMSE 468. Thesis/Article Writing for Scientists and Engineers. 3 Units.

For writing a publication, such as a thesis or a journal article, in a field of science or engineering, students need a diverse set of skills in addition to mastering the scientific content. Generally, scientific writing requires proficiency in document organization, professional presentation of numerical and graphical data, literature retrieval and management, text processing, version control, graphical illustration, the English language, elements of style, etc. For scientists and engineers, specifically, writing a publication requires additional knowledge about e.g. conventions of numerical precision, error limits, mathematical typesetting, proper use of units, proper digital processing of micrographs, etc. Having to acquire these essential skills at the beginning of writing a thesis or a journal article may compromise the outcome by distracting from the most important task of composing the best possible scientific content. This course properly prepares students for scientific writing with a comprehensive spectrum of knowledge, skills, and tools enabling them to fully focus on the scientific content of their thesis or publication when the time has come to start writing. Similar to artistic drawing, where the ability to "see" is as (or more!) important as skills of the hand, the ability of proper scientific writing is intimately linked to the ability of critically reviewing scientific texts. Therefore, students will practice both authoring and critical reviewing of scientific texts. To sharpen students' skills of reviewing, examples of good and less good scientific writing will be taken from published literature in science and engineering and analyzed in the context of knowledge acquired in the course. At the end of the course, students will have set up skills and a highly functional work environment to start writing their role thesis or article with full focus on the scientific content. Students of all disciplines of science and engineering will benefit from the course material. Offered as EMSE 368 and EMSE 468.

EMSE 499. Materials Science and Engineering Colloquium. 0 Unit.

Invited speakers deliver lectures on topics of active research in materials science. Speakers include researchers at universities, government laboratories, and industry. Course is offered only for 0 credits. Attendance is required.

EMSE 500T. Graduate Teaching II. 0 Unit.

To provide teaching experience for all Ph.D.-bound graduate students. This will include preparing exams/quizzes/homework, leading recitation sessions, tutoring, providing laboratory assistance, and developing teaching aids that include both web-based and classroom materials. Graduate students will meet with supervising faculty member throughout the semester. Grading is pass/fail. Students must receive three passing grades and up to two assignments may be taken concurrently. Recommended preparation: Ph.D. student in Materials Science and Engineering.

EMSE 503. Structure of Materials. 3 Units.

The structure of materials and physical properties are explored in terms of atomic bonding and the resulting crystallography. The course will cover basic crystal chemistry, basic crystallography (crystal symmetries, point groups, translation symmetries, space lattices, and crystal classes), basic characterization techniques and basic physical properties related to a materials structure.

EMSE 504. Thermodynamics of Solids. 3 Units.

Review of the first, second, and third laws of thermodynamics and their consequences. Stability criteria, simultaneous chemical reactions, binary and multi-component solutions, phase diagrams, surfaces, adsorption phenomena.

EMSE 505. Phase Transformations, Kinetics, and Microstructure. 3 Units.

Phase diagrams are used in materials science and engineering to understand the interrelationships of composition, microstructure, and processing conditions. The microstructure and phases constitution of metallic and nonmetallic systems alike are determined by the thermodynamic driving forces and reaction pathways. In this course, solution thermodynamics, the energetics of surfaces and interfaces, and both diffusional and diffusionless phase transformations are reviewed. The development of the laws of diffusion and its application for both melts and solids are covered. Phase equilibria and microstructure in multicomponent systems will also be discussed.

EMSE 515. Analytical Methods in Materials Science. 3 Units.

Microcharacterization techniques of materials science and engineering: SPM (scanning probe microscopy), SEM (scanning electron microscopy), FIB (focused ion beam) techniques, SIMS (secondary ion mass spectrometry), EPMA (electron probe microanalysis), XPS (X-ray photoelectron spectrometry), and AES (Auger electron spectrometry), ESCA (electron spectrometry for chemical analysis). The course includes theory, application examples, and laboratory demonstrations.

EMSE 599. Critical Review of Materials Science and Engineering Colloquium. 1 - 2 Units.

Invited speakers deliver lectures on topics of active research in materials science. Speakers include researchers at universities, government laboratories, and industry. Each course offering is for 1 or 2 credits but the course can be taken multiple times totaling up to a maximum of six credits. Attendance is required. Graded coursework is in the form of a term paper per credit. The topic for the term paper(s) should be chosen from seminar topics. The term paper will be graded by the advisor of the graduate student.

EMSE 600T. Graduate Teaching III. 0 Unit.

To provide teaching experience for all Ph.D.-bound graduate students. This will include preparing exam/quizzes/homework, leading recitation sessions, tutoring, providing laboratory assistance, and developing teaching aids that include both web-based and classroom materials. Graduate students will meet with supervising faculty member throughout the semester. Grading is pass/fail. Students must receive three passing grades and up to two assignments may be taken concurrently. Recommended preparation: Ph.D. student in Materials Science and Engineering.

EMSE 601. Independent Study. 1 - 18 Units.

EMSE 634. Special Topics of Materials Science. 1 - 3 Units.

This course introduces graduate students to specific topics of material science, tailored to individual interests of the students. For example, students with interest in specific techniques for microcharacterization of materials may be educated in the physical background of these techniques by studying literature under the guidance of the instructor, presenting and discussing the learned material with the instructor and other students, and being trained in practical experimentation in laboratory sessions demonstrating these techniques on instruments of SCSAM, the Swagelok Center for Surface Analysis of Materials.

EMSE 649. Special Projects. 1 - 18 Units.

EMSE 651. Thesis M.S.. 1 - 18 Units.

Required for Master's degree. A research problem in metallurgy, ceramics, electronic materials, biomaterials or archeological and art historical materials, culminating in the writing of a thesis.

EMSE 695. Project M.S.. 1 - 9 Units.

Research course taken by Plan B M.S. students. Prereq: Enrolled in the EMSE Plan B MS Program.

EMSE 701. Dissertation Ph.D.. 1 - 9 Units.

Required for Ph.D. degree. A research problem in metallurgy, ceramics, electronic materials, biomaterials or archeological and art historical materials, culminating in the writing of a thesis. Prereq: Predoctoral research consent or advanced to Ph.D. candidacy milestone.