23 citations · 23 across the 10 of their papers we have counts for
10 papers
Deep Research in Physical Sciences: A Multi-Agent Framework and Comprehensive Benchmark
Yigeng Jiang, Tengchao Yang, Taoyong Cui +25
Deep research agents are Large Language Model (LLM)-based systems designed for autonomous, multi-step scientific reasoning, and they hold immense potential for accelerating researc…
Equivariant Evidential Deep Learning for Interatomic Potentials
Zhongyao Wang, Taoyong Cui, Jiawen Zou +5
Uncertainty quantification (UQ) is critical for assessing the reliability of machine learning interatomic potentials (MLIPs) in molecular dynamics (MD) simulations, identifying ext…
Probing Scientific General Intelligence of LLMs with Scientist-Aligned Workflows
Wanghan Xu, Yuhao Zhou, Yifan Zhou +104
Despite advances in scientific AI, a coherent framework for Scientific General Intelligence (SGI)-the ability to autonomously conceive, investigate, and reason across scientific do…
Iterative Pretraining Framework for Interatomic Potentials
Taoyong Cui, Zhongyao Wang, Dongzhan Zhou +5
Machine learning interatomic potentials (MLIPs) enable efficient molecular dynamics (MD) simulations with ab initio accuracy and have been applied across various domains in physica…
HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials
Taoyong Cui, Yunhong Han, Haojun Jia +2
Transition state (TS) characterization is central to computational reaction modeling, yet conventional approaches depend on expensive density functional theory (DFT) calculations,…
Evidential Deep Learning for Interatomic Potentials
Han Xu, Taoyong Cui, Chenyu Tang +8
Machine learning interatomic potentials (MLIPs) have been widely used to facilitate large-scale molecular simulations with accuracy comparable to ab initio methods. In practice, ML…
Online Test-time Adaptation for Interatomic Potentials
Taoyong Cui, Chenyu Tang, Dongzhan Zhou +5
Machine learning interatomic potentials (MLIPs) enable more efficient molecular dynamics (MD) simulations with ab initio accuracy, which have been used in various domains of physic…
Geometry-enhanced Pre-training on Interatomic Potentials
Taoyong Cui, Chenyu Tang, Mao Su +6
Machine learning interatomic potentials (MLIPs) enables molecular dynamics (MD) simulations with ab initio accuracy and has been applied to various fields of physical science. Howe…
Contrastive Learning for Low-light Raw Denoising
Taoyong Cui, Yuhan Dong
Image/video denoising in low-light scenes is an extremely challenging problem due to limited photon count and high noise. In this paper, we propose a novel approach with contrastiv…
TAOTF: A Two-stage Approximately Orthogonal Training Framework in Deep Neural Networks
Taoyong Cui, Jianze Li, Yuhan Dong +1
The orthogonality constraints, including the hard and soft ones, have been used to normalize the weight matrices of Deep Neural Network (DNN) models, especially the Convolutional N…