(395p) Empowering Student-Led Explorations in Computational Materials Science

California State University, Monterey Bay (CSUMB) is a public, Hispanic-Serving Institution (HSI) with a recently launched Mechatronics Engineering major. As part of a strategic initiative to broaden curricular offerings and support the development of a future Materials Science minor, we designed and implemented a course titled 'Computational Materials Science.' This course addresses the growing need for accessible, interdisciplinary training in computational modeling, quantum chemistry, and data science, especially in institutions serving diverse and underrepresented student populations. The course integrates quantum chemistry, density functional theory (DFT), reactive molecular dynamics (MD), and data science within a practical and accessible computational framework. Students gained hands-on experience using industry-standard software packages such as Gaussian16 and SCM (Amsterdam Modeling Suite), covering topics including geometry optimization, electronic structure analysis, vibrational frequency analysis, and reactive molecular dynamics (ReaxFF).

An innovative feature of this course was the implementation of student-driven projects, where students selected from various molecular systems—including coronenes, graphene analogs, and functionalized polymers—each with targeted applications in energy storage, sensors, and electronic materials. Through these projects, students engaged deeply in predictive modeling, materials optimization, and performance assessment under realistic conditions. The mid-term project served as a pivotal learning milestone. Students were asked to select a class of molecules based on structural or functional themes—options included extended aromatic hydrocarbons (e.g., coronenes), small organic acids, sulfonates, and potential polymer precursors. Each molecular class was paired with real-world applications such as supercapacitor electrodes, CO₂ capture systems, biodegradable plastics, and ion-exchange membranes. Students were tasked with conducting full DFT workflows using SCM or Gaussian, including geometry optimization, vibrational frequency analysis, and partial charge analysis. For systems undergoing bond formation or dissociation, students employed ReaxFF-based reactive MD simulations to explore energy landscapes and reaction pathways. In their written and oral reports, students compared different candidate molecules in their chosen class, identifying tradeoffs in electronic properties, reactivity, and stability, and proposing follow-up simulations.

This course is strategically embedded within our Mechatronics Engineering curriculum and serves as an upper level elective for all STEM majors. The learning experience enhances multidisciplinary integration by combining mechanical systems, electronics, and software control with materials-focused computational methods. Furthermore, it serves as a foundational component in the ongoing development of a new Materials Science minor at our institution. Preliminary assessment results indicate substantial improvements in student computational literacy, analytical skills in materials science, and confidence in data-driven decision-making. The successful integration of advanced computational techniques into the engineering curriculum presented here offers a valuable and replicable model for institutions aiming to broaden their educational offerings in computational materials science.