23 citations · 26 across the 11 of their papers we have counts for
11 papers
Active rejection enables reliable generalization of universal machine-learning interatomic potentials
Mingxiang Luo, Xinnan Mao, Lu Wang +3
Universal machine learning interatomic potentials (uMLIPs) bridge quantum-mechanical accuracy and large-scale molecular dynamics, but the cost of high-accuracy calculations such as…
Pessimistic Risk-Aware Policy Learning in Contextual Bandits
Yilong Wan, Yuqiang Li, Xianyi Wu
We study risk-aware offline policy learning, aiming to learn a decision rule from logged data that is optimal under general risk criteria. This problem is crucial in high-stakes do…
Benchmarking virtual cell models for in-the-wild perturbation response
Xinjie Mao, Songming Zhang, Qianhong Wen +10
Virtual cell (VC) models aim to predict cellular responses to any perturbations in silico and have emerged as a promising approach for drug discovery and precision medicine. Yet, a…
NMRGym: A Comprehensive Benchmark for Nuclear Magnetic Resonance Based Molecular Structure Elucidation
Zheng Fang, Chen Yang, Hai-tao Yu +5
Nuclear Magnetic Resonance (NMR) spectroscopy is the cornerstone of small-molecule structure elucidation. While deep learning has demonstrated significant potential in automating s…
VASP Agent: An Agentic Framework for Autonomous First-principles Calculations
Zeyu Xia, Jinzhe Ma, Congjie Zheng +11
Large Language Models (LLMs) are increasingly embedded in agentic frameworks for scientific discovery. First-principles materials computation imposes a demanding standard for auton…
ChemBOMAS: Accelerated BO in Chemistry with LLM-Enhanced Multi-Agent System
Dong Han, Zhehong Ai, Pengxiang Cai +16
Bayesian optimization (BO) is a powerful tool for scientific discovery in chemistry, yet its efficiency is often hampered by the sparse experimental data and vast search space. Her…
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…
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…
Physical formula enhanced multi-task learning for pharmacokinetics prediction
Ruifeng Li, Dongzhan Zhou, Ancheng Shen +9
Artificial intelligence (AI) technology has demonstrated remarkable potential in drug dis-covery, where pharmacokinetics plays a crucial role in determining the dosage, safety, and…
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…