Enze Chen

All Publications

• Topological grain boundary segregation transitions. Science (New York, N.Y.) Devulapalli, V., Chen, E., Brink, T., Frolov, T., Liebscher, C. H. 2024; 386 (6720): 420-424

  Abstract

  Engineering the structure of grain boundaries (GBs) by solute segregation is a promising strategy to tailor the properties of polycrystalline materials. Solute segregation triggering phase transitions at GBs has been suggested theoretically to offer different pathways to design interfaces, but an understanding of their intrinsic atomistic nature is missing. We combined atomic resolution electron microscopy and atomistic simulations to discover that iron segregation to GBs in titanium stabilizes icosahedral units ("cages") that form robust building blocks of distinct GB phases. Owing to their five-fold symmetry, the iron cages cluster and assemble into hierarchical GB phases characterized by a different number and arrangement of the constituent icosahedral units. Our advanced GB structure prediction algorithms and atomistic simulations validate the stability of these observed phases and the high excess of iron at the GB that is accommodated by the phase transitions.

  View details for DOI 10.1126/science.adq4147

  View details for PubMedID 39446949

• 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

• Educating current industrial workforce to embrace data-driven materials development MRS BULLETIN Chen, E., Igbineweka, O., Kubie, L., Peerless, J. S. 2022; 47 (10): 981-985

  View details for DOI 10.1557/s43577-022-00410-6

  View details for Web of Science ID 000864040300001

• 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 Ni₃Al-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

• Stitch It Up: Using Progressive Data Storage to Scale Science Lofstead, J., Mitchell, J., Chen, E., IEEE IEEE. 2020: 52-61

  View details for DOI 10.1109/IPDPS47924.2020.00016

  View details for Web of Science ID 000643734800006

• 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