3 papers
cs.LG2026
Multigrid Training for Molecular Generation using Graph Neural Networks
Zixuan Ling, Paula Mercurio, Di Liu
Deep learning has demonstrated significant success for modeling biochemical molecular systems, where inputs are commonly represented as graphs or 3D grids. A major challenge is tha…
q-bio.BM2024
Clustering Molecular Energy Landscapes by Adaptive Network Embedding
Paula Mercurio, Di Liu
In order to efficiently explore the chemical space of all possible small molecules, a common approach is to compress the dimension of the system to facilitate downstream machine le…
math.NA2020
Identifying Transition States of Chemical Kinetic Systems using Network Embedding Techniques
Paula Mercurio, Di Liu
Using random walk sampling methods for feature learning on networks, we develop a method for generating low-dimensional node embeddings for directed graphs and identifying transiti…