23 citations · 28 across the 8 of their papers we have counts for
5 papers · 1 filter
Deep Learning of ab initio Hessians for Transition State Optimization
Eric C. -Y. Yuan, Anup Kumar, Xingyi Guan +5
Identifying transition states -- saddle points on the potential energy surface connecting reactant and product minima -- is central to predicting kinetic barriers and understanding…
Beyond potential energy surface benchmarking: a complete application of machine learning to chemical reactivity
Xingyi Guan, Joseph Heindel, Taehee Ko +2
We train an equivariant machine learning model to predict energies and forces for a real-world study of hydrogen combustion under conditions of finite temperature and pressure. Thi…
Using Diffusion Maps to Analyze Reaction Dynamics for a Hydrogen Combustion Benchmark Dataset
Taehee Ko, Joseph Heindel, Xingyi Guan +3
We use local diffusion maps to assess the quality of two types of collective variables (CVs) for a recently published hydrogen combustion benchmark dataset~\cite{guan2022benchmark}…
NewtonNet: A Newtonian message passing network for deep learning of interatomic potentials and forces
Mojtaba Haghighatlari, Jie Li, Xingyi Guan +10
We report a new deep learning message passing network that takes inspiration from Newton's equations of motion to learn interatomic potentials and forces. With the advantage of dir…
Learning to Make Chemical Predictions: the Interplay of Feature Representation, Data, and Machine Learning Algorithms
Mojtaba Haghighatlari, Jie Li, Farnaz Heidar-Zadeh +3
Recently supervised machine learning has been ascending in providing new predictive approaches for chemical, biological and materials sciences applications. In this Perspective we…