@prakash11.bsky.social
Reposted
Check out our paper on designing spinodal metamaterials by directly using a small set of experimental data! We introduce physics-enhanced ML strategies to compensate for the small amount of data and simulation-experiment gap.👇
doi.org/10.1016/j.em...
doi.org/10.1016/j.em...
Starting 2025 w/ a collaborative effort w/ @sid-kumar.bsky.social & team using experimental data sets, physics-enhanced ML, and computational models to predict, understand, and inversely design spinodal metamaterials for large deformation and energy absorption. doi.org/10.1016/j.em...
January 7, 2025 at 7:07 AM
Check out our paper on designing spinodal metamaterials by directly using a small set of experimental data! We introduce physics-enhanced ML strategies to compensate for the small amount of data and simulation-experiment gap.👇
doi.org/10.1016/j.em...
doi.org/10.1016/j.em...
Reposted
Interested in designing recyclable polymers with human-in-the-loop ML — from computational predictions all the way to lab synthesis? Check out our latest paper 👇
doi.org/10.1002/advs...
Great collaboration with Aniruddh Vashisth and group @ University of Washington!
doi.org/10.1002/advs...
Great collaboration with Aniruddh Vashisth and group @ University of Washington!
AI‐Guided Inverse Design and Discovery of Recyclable Vitrimeric Polymers
This study presents a molecular dynamics (MD) - machine learning (ML) framework for inverse design of vitrimers with targeted glass transition temperature (Tg). A variational autoencoder model with d...
doi.org
December 17, 2024 at 11:34 PM
Interested in designing recyclable polymers with human-in-the-loop ML — from computational predictions all the way to lab synthesis? Check out our latest paper 👇
doi.org/10.1002/advs...
Great collaboration with Aniruddh Vashisth and group @ University of Washington!
doi.org/10.1002/advs...
Great collaboration with Aniruddh Vashisth and group @ University of Washington!