Engineering programs in the United States continue to struggle with high attrition and limited diversity. This study investigates an unconventional solution: a narrative-based intervention where students write and perform their own engineering stories to build resilience and improve retention. While engineering curricula typically emphasize technical rigor, this research explores how a storytelling intervention influenced undergraduate students’ perceptions of their sense of belonging, professional identity, and persistence. The intervention, implemented across six semesters in diverse engineering disciplines, engaged students in developing and performing personal stories about their engineering journeys. Utilizing inductive thematic analysis of semi-structured interviews with 17 participants, we investigated how the narrative process influenced student perceptions. Results: Participants reported that the intervention positively influenced their identity development. Most participants reported a strengthened or reaffirmed intention to remain in the field. Our findings suggest that storytelling provided students with a structured way to discuss struggle, connect with peers, and see themselves more clearly as part of engineering. Those shifts may matter for retention, especially for students who otherwise feel isolated.

This systematic literature review investigates how inclusive curricular interventions and pedagogy in undergraduate engineering education influence sense of belonging, professional identity, and persistence among historically underserved students. Analyzing 40 peer-reviewed studies published between 2000 and 2024, we employed thematic analysis across two coding cycles to identify patterns in intervention types and outcomes. Multiple curricular themes emerged: fostering personal connections through representation and mentorship; implementing student-centered pedagogies; cultivating collective responsibility for inclusivity; increasing structural access; empowering students through introspection and critical analysis; reframing narratives of belonging; and leveraging professional engineering organizations. These interventions yielded three primary outcomes: cultivating community cultural wealth (navigational, social, aspirational, familial, resistance, and linguistic capital), fostering inclusive engineering identities characterized by heightened awareness of systemic barriers, and improving belonging, professional identity, and persistence intentions. Identity-matched mentorship and participation in identity-based organizations significantly enhanced retention among women and underrepresented minority students. However, findings reveal a persistent gap between students’ awareness of inequities and their translation of this knowledge into equitable practice. We conclude that isolated interventions are insufficient; inclusive curricula must be integrated longitudinally across engineering education. Future research should employ rigorous mixed-methods designs with larger samples to establish efficacy and facilitate replication.

Predicting the mechanical strength of thermoplastic composites joined through fusion welding with computer simulation is challenging because the long timescales of entanglement formation are difficult to access with atomistically detailed models. Coarse-grained models provide one route to speeding up the sampling of entanglements, but because they abstract away atomistic details of the modeled molecules extra care must be taken to validate their structural and thermomechanical predictions across relevant state space. This work presents new results using a modeling framework for creating, validating, and using coarse-grained models to study fusion welding processes. We demonstrate our software on case studies of poly(phenylene sulfide) and poly(ether ketone ketone) (PPS and PEKK). We demonstrate coarse models of these two chemistries learned through multi-state iterative Boltzmann inversion. The models are validated against OPLS-UA models, and against experiments through agreement of density, structure, relaxation dynamics, and glass transition temperature. We demonstrate fusion welding process modeling with coarse polymers and conclude with a discussion of tradeoffs between performance and accuracy, and opportunities for future work.

We incorporated Espaloma forcefield parameterization into MoSDeF tools for performing molecular dynamics simulations of organic molecules with HOOMD-Blue. We compared equilibrium morphologies predicted for perylene and poly-3-hexylthiophene (P3HT) with the ESP-UA forcefield in the present work against prior work using the OPLS-UA forcefield. We found that, after resolving the chemical ambiguities in molecular topologies, ESP-UA is similar to GAFF. We observed the clustering/melting phase behavior to be similar between ESP-UA and OPLS-UA, but the base energy unit of OPLS-UA was found to better connect to experimentally measured transition temperatures. Short-range ordering measured by radial distribution functions was found to be essentially identical between the two forcefields, and the long-range ordering measured by grazing incidence X-ray scattering was qualitatively similar, with ESP-UA matching experiments better than OPLS-UA. We concluded that Espaloma offers promise in the automated screening of molecules that are from more complex chemical spaces.

Carbon-fiber composites with thermoplastic matrices offer many processing and performance benefits in aerospace applications, but the long relaxation times of polymers make it difficult to predict how the structure of the matrix depends on its chemistry and how it was processed. Coarse-grained models of polymers can enable access to these long-time dynamics, but can have limited applicability outside the systems and state points that they are validated against. Here we develop and validate a minimal coarse-grained model of the aerospace thermoplastic poly(etherketoneketone) (PEKK). We use multistate iterative Boltzmann inversion to learn potentials with transferability across thermodynamic states relevant to PEKK processing. We introduce tabulated EKK angle potentials to represent the ratio of terephthalic (T) and isophthalic (I) acid precursor amounts, and validate against rheological experiments: The glass transition temperature is independent to T/I, but chain relaxation and melting temperature is. In sum we demonstrate a simple, validated model of PEKK that offers 15× performance speedups over united atom representations that enables studying thermoplastic processing-structure-property-performance relationships.

Molecular simulations are increasingly used to predict thermophysical properties and explore molecular-level phenomena beyond modern imaging techniques. To make these tools accessible to nonexperts, several open-source molecular dynamics (MD) and Monte Carlo (MC) codes have been developed. However, using these tools is challenging, and concerns about the validity and reproducibility of the simulation data persist. In 2017, Schappals et al. reported a benchmarking study involving several research groups independently performing MD and MC simulations using different software to predict densities of alkanes using common molecular mechanics force fields [J. Chem. Theory Comput.2017, 4270-4280]. Although the predicted densities were reasonably close (mostly within 1%), the data often fell outside of the combined statistical uncertainties of the different simulations. Schappals et al. concluded that there are unavoidable errors inherent to molecular simulations once a certain degree of complexity of the system is reached. The Molecular Simulation Design Framework (MoSDeF) is a workflow package designed to achieve TRUE (Transparent, Reproducible, Usable-by-others, and Extensible) simulation studies by standardizing the implementation of molecular models for various simulation engines. This work demonstrates that using MoSDeF to initialize a simulation workflow results in consistent predictions of system density, even while increasing model complexity.

We propose new definitions of integral, reduced, and normal superrings and superschemes to properly establish the notion of a supervariety. We generalize several results about classical reduced rings and varieties to the supergeometric setting, including an equivalence of categories between certain toric supervarieties and decorated polyhedral fans. These decorated fans are shown to encode important geometric information about the corresponding toric supervarieties. We then investigate some naturally-occurring toric supervarieties inside the isomeric supergrassmannian, which we show admits a nice description as a decorated polytope.

We provide an elementary proof that with the exceptions of certain Π-projective spaces, both the Picard group and the Π-Picard set of the isomeric (i.e. type-Q) supergrassmannian are trivial. We extend this technique to show that the Picard group and the Π-Picard set of a supertorus orbit closure within the isomeric supergrassmannian can be easily calculated from its defining polytope by counting the number of simplex factors. Since the presence of nontrivial invertible sheaves and Π-invertible sheaves depends entirely on factors of Π-projective space, we construct them as symmetric powers of the tautological sheaf and its dual.

In this work describe a project that simultaneously attempts to meet ABET outcome 3 (an ability to communicate effectively with a range of audiences), improve retention of at-risk students, and understand how students in an undergraduate materials curriculum develop their professional identity. This project is inspired by prior research showing that self-identification with one's major is the strongest predictor of degree completion, especially among women and minority students in STEM. We collaborate with The Story Collider, a national science storytelling nonprofit, to provide story development instruction and feedback to juniors taking Thermodynamics of Materials. We test a single storytelling intervention: An assignment in which students develop "True, personal stories about a time thermodynamics happened", and measure student attitudes with a Likert scale survey before and after the intervention. Preliminary results across two cohorts indicate increases in self-identification as materials scientists. We also discuss plans for measuring long-term success and retention as well as the impacts of hosting a public Story Collider wherein a selection of stories are performed live.

In this paper we describe the notion of a toric supervariety, generalizing that of a toric variety from the classical setting. We give a combinatorial interpretation of the category of quasinormal toric supervarieties with one odd dimension using decorated polyhedral fans. We then use this interpretation to calculate some invariants of these supervarieties and extract geometric information from them.

Student persistence in STEM programs is linked to student sense of belonging and identification with their major or profession. Lack of professional identification and lack of belonging exacerbate departures of Black, Latinx, Native, and Female students from STEM programs, for whom exclusionary department cultures and biased policies have amplified impact. This workshop provides an overview of the science and craft of storytelling which has been developed with The Story Collider.

It is known (see e.g. [2], [4], [5], [6]) that continuous variations in the entries of a complex square matrix induce continuous variations in its eigenvalues. If such a variation arises from one real parameter $\alpha \in [0, 1]$, then the eigenvalues follow continuous paths in the complex plane as ${\alpha}$ shifts from $0$ to $1$. The intent here is to study the nature of these eigenpaths, including their behavior under small perturbations of the matrix variations, as well as the resulting eigenpairings of the matrices that occur at ${\alpha} = 0$ and ${\alpha} = 1$. We also give analogs of our results in the setting of monic polynomials.

Quantum dots that store large tensile strains represent an emerging research area. We combine experiments and computational modeling to investigate the self-assembly of Ge and GaAs tensile-strained quantum dots (TSQDs) on In0.52Al0.48As-(111)A Comparing these two nominally similar material systems highlights how differences in adatom kinetics leads to distinct features of Ge and GaAs TSQD self-assembly. The energy barrier to diffusion of Ge adatoms is higher than that for Ga adatoms, while forming a stable island requires six Ge atoms and four Ga atoms. Unusually, these critical cluster sizes do not increase as we raise the substrate temperature. Radial distribution scaling shows that both Ge and GaAs TSQDs preferentially nucleate at a particular distance from their neighbors. This deeper understanding of the physics of Ge(111) and GaAs(111)A TSQD self-assembly will enable researchers to more effectively tailor these nanostructures to specific optoelectronic applications.

The signac data management framework (https://signac.io) helps researchers execute reproducible computational studies, scales workflows from laptops to supercomputers, and emphasizes portability and fast prototyping. With signac, users can track, search, and archive data and metadata for filebased workflows and automate workflow submission on high performance computing (HPC) clusters. We will discuss recent improvements to the software’s feature set, scalability, scientific applications, usability, and community. Newly implemented synced data structures, features for generalized workflow execution, and performance optimizations will be covered, as well as recent research using the framework and changes to the project’s outreach and governance as a response to its growth.

Making materials out of buckminsterfullerene is challenging, because it requires first dispersing the molecules in a solvent, and then getting the molecules to assemble in the desired arrangements. In this computational work, we focus on the dispersion challenge: How can we conveniently solubilize buckminsterfullerene? Water is a desirable solvent because of its ubiquity and biocompatibility, but its polarity makes the dispersion of nonpolar fullerenes challenging. We perform molecular dynamics simulations of fullerenes in the presence of fullerene oxides in implicit water to elucidate the role of interactions (van der Waals and Coulombic) on the self-assembly and structure of these aqueous mixtures. Seven coarse-grained fullerene models are characterized over a range of temperatures and interaction strengths using HOOMD-Blue on high performance computing clusters. We find that dispersions of fullerenes stabilized by fullerene oxides are observable in models where the net attraction among fullerenes is about 1.5 times larger than the at-tractions between oxide molecules. We demonstrate that simplified models are sufficient for qualitatively modeling micellization of these fullerenes and provide an efficient starting point for investigating how structural details and phase behavior depend upon the inclusion of more detailed physics.

Molecular simulation has emerged as an important sub-field of chemical engineering, due in no small part to the leadership of Keith Gubbins. A characteristic of the chemical engineering molecular simulation community is the commitment to freely share simulation codes and other key software components required to perform a molecular simulation under open-source licenses and distribution on public repositories such as GitHub. Here we provide an overview of open-source molecular modeling software in Chemical Engineering, with focus on the Molecular Simulation Design Framework (MoSDeF). MoSDeF is an open-source Python software stack that enables facile use of multiple open-source molecular simulation engines, while at the same time ensuring maximum reproducibility.