1 citations · 2 across the 4 of their papers we have counts for
4 papers
A Generalized Approach for Incorporating Geometry and Directionality into Coarse-Grained Machine-Learned Potentials
Arthur Y. Lin, Tejas Dahiya, Rose K. Cersonsky
Machine-learned interatomic potentials have enabled highly accurate atomistic simulations, but extending these capabilities to coarse-grained systems remains challenging due to the…
Extrapolation of Machine-Learning Interatomic Potentials for Organic and Polymeric Systems
Natalie E. Hooven, Arthur Y. Lin, Charles H. Carroll +1
Machine-Learning Interatomic Potentials (MLIPs) have surged in popularity due to their promise of expanding the spatiotemporal scales possible for simulating molecules with high fi…
Interpretable Visualizations of Data Spaces for Classification Problems
Christian Jorgensen, Arthur Y. Lin, Rhushil Vasavada +1
How do classification models "see" our data? Based on their success in delineating behaviors, there must be some lens through which it is easy to see the boundary between classes;…
Expanding Density-Correlation Machine Learning Representations for Anisotropic Coarse-Grained Particles
Arthur Y. Lin, Kevin K. Huguenin-Dumittan, Yong-Cheol Cho +2
Physics-based, atom-centered machine learning (ML) representations have been instrumental to the effective integration of ML within the atomistic simulation community. Many of thes…