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)

All Publications

  • ARTIFICIAL INTELLIGENCE IN MATERIALS EDUCATION: A ROUNDTABLE DISCUSSION JOM Tyler, K., Chen, E., Meredig, B., Sparks, T. 2023; 75 (7): 2083-2085
  • Using Jupyter Tools to Design an Interactive Textbook to Guide Undergraduate Research in Materials Informatics JOURNAL OF CHEMICAL EDUCATION Chen, E., Asta, M. 2022
  • 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)
  • 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


    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