Our recent work on applying QM-MM method to estimate grain boundary segregation energy distributions is featured in Nature Computational Science! Read more about the work here.
Nanocrystalline metals are highly defected structures that accelerate sintering. We study the kinetic competition between low-temperature densification and organic removal and develop a grain size dependent map, which allows to predict the sintering behavior of a broad range of materials.
Schuh group will give talks at TMS 2023. Save the dates below! (sorted by date) Submicron intermetallic particle heterogeneity controls shear localization in high-strength nanostructured Al alloys, Tianjiao Lei; Esther C. Hessong; Jungho Shin; Daniel S. Gianola; Timothy J. Rupert:
Grain boundaries contain a broad range of segregation sites. We have estimated distributions of grain boundary segregation entropy, and found a correlation between site-wise segregation energy and entropy. The spectra along with a spectral thermodynamic model shall provide an insight
Our new publication introduces a new launch pad architecture for laser induced particle impact testing (LIPIT) that brings some exciting new capabilities to the method. Read more about it in Small Methods. Read more on Small Methods!
Check out our talks at MRS Fall 2022! (sorted by date) Malik Wagih: 9:45 AM, Tuesday Morning, November 29, 2022, Sheraton 3rd floor Berkeley, SF04.01.04 Towards Quantum-Accurate Modeling of Polycrystalline Grain Boundary Environments Yannick Naunheim: 3:45 PM, Tuesday Afternoon, November
Come see PhD candidate Thomas Matson present his talk, “Atomistic Assessment of the Solute-Solute Interaction Spectrum in a Polycrystal,” at MSE Congress 2022 in Darmstadt, Germany – September 27-29, 2022.
Come see PhD Candidate Tyler Lucas present his talk, “In-Situ Analysis and Modeling of High Velocity Microparticle Impacts On Tin,” at GSCCM (Group for Shock Compression of Condensed Matter) in Anaheim, California – July 10-15, 2022.
Our new publication on shows how to overcome the limited availability of interatomic potentials by combining machine learning with first-principles calculations to study grain boundary solute segregation. Read more on Physical Review Letter here!
Study shows what happens when crystalline grains in metals reform at nanometer scales, improving metal properties. Read the full article by David L. Chandler here.