46 citations · 75 across the 18 of their papers we have counts for
5 papers · 1 filter
ChemVLM: Exploring the Power of Multimodal Large Language Models in Chemistry Area
Junxian Li, Di Zhang, Xunzhi Wang +16
Large Language Models (LLMs) have achieved remarkable success and have been applied across various scientific fields, including chemistry. However, many chemical tasks require the…
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…
ChemLLM: A Chemical Large Language Model
Di Zhang, Wei Liu, Qian Tan +12
Large language models (LLMs) have made impressive progress in chemistry applications. However, the community lacks an LLM specifically designed for chemistry. The main challenges a…