13 citations · 18 across the 8 of their papers we have counts for
8 papers
cuGUGA: Operator-Direct Graphical Unitary Group Approach Accelerated with CUDA
Zihan Pengmei
We present cuGUGA, an operator-direct graphical unitary group approach (GUGA) configuration interaction (CI) solver in a spin-adapted configuration state function (CSF) basis. Dyna…
Hierarchical geometric deep learning enables scalable analysis of molecular dynamics
Zihan Pengmei, Spencer C. Guo, Chatipat Lorpaiboon +1
Molecular dynamics simulations can generate atomically detailed trajectories of complex systems, but analyzing these dynamics can be challenging when systems lack well-established…
The Kinetics of Reasoning: How Chain-of-Thought Shapes Learning in Transformers?
Zihan Pengmei, Costas Mavromatis, Zhengyuan Shen +3
Chain-of-thought (CoT) supervision can substantially improve transformer performance, yet the mechanisms by which models learn to follow and benefit from CoT remain poorly understo…
Pushing the Limits of All-Atom Geometric Graph Neural Networks: Pre-Training, Scaling and Zero-Shot Transfer
Zihan Pengmei, Zhengyuan Shen, Zichen Wang +2
Constructing transferable descriptors for conformation representation of molecular and biological systems finds numerous applications in drug discovery, learning-based molecular dy…
Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamics
Zihan Pengmei, Chatipat Lorpaiboon, Spencer C. Guo +2
Identifying informative low-dimensional features that characterize dynamics in molecular simulations remains a challenge, often requiring extensive manual tuning and system-specifi…
Transformers are efficient hierarchical chemical graph learners
Zihan Pengmei, Zimu Li, Chih-chan Tien +2
Transformers, adapted from natural language processing, are emerging as a leading approach for graph representation learning. Contemporary graph transformers often treat nodes or e…