3 papers
cond-mat.mtrl-sci2026
Bias in Universal Machine-Learned Interatomic Potentials and its Effects on Fine-Tuning
Nicolas Wong, Julia H. Yang
Universal machine learned interatomic potentials (uMLIPs) embody a growing area of interest due to their transferability across the periodic table, displaying an error of about 0.6…
cond-mat.mtrl-sci2025
A practical guide to machine learning interatomic potentials -- Status and future
Ryan Jacobs, Dane Morgan, Siamak Attarian +27
The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not exper…
physics.chem-ph2025
Room-temperature decomposition of the ethaline deep eutectic solvent
Julia H. Yang, Amanda Whai Shin Ooi, Zachary A. H. Goodwin +5
Environmentally-benign, non-toxic electrolytes with combinatorial design spaces are excellent candidates for green solvents, green leaching agents, and carbon capture sources. Here…