Clustering Molecular Energy Landscapes by Adaptive Network Embedding

no code implementations • 19 Jan 2024 • 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 learning tasks.

Clustering Network Embedding

Network Embedding Using Sparse Approximations of Random Walks

no code implementations • 25 Aug 2023 • Paula Mercurio, Di Liu

In this paper, we propose an efficient numerical implementation of Network Embedding based on commute times, using sparse approximation of a diffusion process on the network obtained by a modified version of the diffusion wavelet algorithm.

Clustering Multi-Label Classification +1

Identifying Transition States of Chemical Kinetic Systems using Network Embedding Techniques

no code implementations • 29 Oct 2020 • 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 transition states of stochastic chemical reacting systems.

Dimensionality Reduction Network Embedding