Bio

Enze (he/him, '18) is a Lecturer in Materials Science and Engineering (MSE) who teaches a variety of undergraduate MSE courses spanning structure, characterization, energy, and computing. He obtained his PhD in MSE from UC Berkeley, where his research applied computational tools to study planar defects and materials informatics education. He is excited to return to The Farm and to help advance student success.

Academic Appointments

Professional Education

  • Ph.D., University of California, Berkeley, Materials Science and Engineering (2023)
  • M.S., Stanford University, Computational and Mathematical Engineering (2018)
  • B.S., Stanford University, Materials Science and Engineering (2018)

Contact

  • Academic
    enze@stanford.edu
    University - Academic staff Department: Materials Science and Engineering Position: Lecturer
    • Durand Building, Room 113
    • Stanford,  California  94305 

Additional Info

Links

All Publications

  • Grand canonically optimized grain boundary phases in hexagonal close-packed titanium. Nature communications Chen, E., Heo, T. W., Wood, B. C., Asta, M., Frolov, T. 2024; 15 (1): 7049

    Abstract

    Grain boundaries (GBs) profoundly influence the properties and performance of materials, emphasizing the importance of understanding the GB structure and phase behavior. As recent computational studies have demonstrated the existence of multiple GB phases associated with varying the atomic density at the interface, we introduce a validated, open-source GRand canonical Interface Predictor (GRIP) tool that automates high-throughput, grand canonical optimization of GB structures. While previous studies of GB phases have almost exclusively focused on cubic systems, we demonstrate the utility of GRIP in an application to hexagonal close-packed titanium. We perform a systematic high-throughput exploration of tilt GBs in titanium and discover previously unreported structures and phase transitions. In low-angle boundaries, we demonstrate a coupling between point defect absorption and the change in the GB dislocation network topology due to GB phase transformations, which has important implications for the accommodation of radiation-induced defects.

    View details for DOI 10.1038/s41467-024-51330-9

    View details for PubMedID 39147757

    View details for PubMedCentralID PMC11327258

  • ARTIFICIAL INTELLIGENCE IN MATERIALS EDUCATION: A ROUNDTABLE DISCUSSION JOM Tyler, K., Chen, E., Meredig, B., Sparks, T. 2023; 75 (7): 2083-2085

    View details for DOI 10.1007/s11837-023-05922-3

    View details for Web of Science ID 001000516100009

  • Using Jupyter Tools to Design an Interactive Textbook to Guide Undergraduate Research in Materials Informatics JOURNAL OF CHEMICAL EDUCATION Chen, E., Asta, M. 2022

    View details for DOI 10.1021/acs.jchemed.2c00640

    View details for Web of Science ID 000858465700001

  • Modeling antiphase boundary energies of Ni<sub>3</sub>Al-based alloys using automated density functional theory and machine learning NPJ COMPUTATIONAL MATERIALS Chen, E., Tamm, A., Wang, T., Epler, M. E., Asta, M., Frolov, T. 2022; 8 (1)

    View details for DOI 10.1038/s41524-022-00755-1

    View details for Web of Science ID 000788033000002

  • Transferable Kinetic Monte Carlo Models with Thousands of Reactions Learned from Molecular Dynamics Simulations. The journal of physical chemistry. A Chen, E., Yang, Q., Dufour-Decieux, V., Sing-Long, C. A., Freitas, R., Reed, E. J. 2019

    Abstract

    Molecular dynamics (MD) simulation of complex chemistry typically involves thousands of atoms propagating over millions of time steps, generating a wealth of data. Traditionally these data are used to calculate some aggregate properties of the system and then discarded, but we propose that these data can be reused to study related chemical systems. Using approximate chemical kinetic models and methods from statistical learning, we study hydrocarbon chemistries under extreme thermodynamic conditions. We discover that a single MD simulation can contain sufficient information about reactions and rates to predict the dynamics of related yet different chemical systems using kinetic Monte Carlo (KMC) simulation. Our learned KMC models identify thousands of reactions and run 4 orders of magnitude faster than MD. The transferability of these models suggests that we can viably reuse data from existing MD simulations to accelerate future simulation studies and reduce the number of new MD simulations required.

    View details for PubMedID 30735373

https://orcid.org/0000-0002-7621-115X