203 citations · 219 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials
Zachary A. H. Goodwin, Malia B. Wenny, Julia H. Yang +11
Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from energy storage to solvents, where they have been touted as ``designer solvents'' a…