1 citations · 1 across the 4 of their papers we have counts for
8 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…
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…
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…
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…