3 papers
physics.chem-ph2026
Molecular electrostatic potentials from machine learning models for dipole and quadrupole predictions
Kadri Muuga, Lisanne Knijff, Chao Zhang
The molecular electrostatic potential (MEP) is a key quantity for describing and predicting intermolecular and ion-molecule interactions. Here, we assess the ability of machine-lea…
cond-mat.stat-mech2024
PiNNAcLe: Adaptive Learn-On-The-Fly Algorithm for Machine-Learning Potential
Yunqi Shao, Chao Zhang
PiNNAcLe is an implementation of our adaptive learn-on-the-fly algorithm for running machine-learning potential (MLP)-based molecular dynamics (MD) simulations -- an emerging appro…
cond-mat.mtrl-sci2024
LLMatDesign: Autonomous Materials Discovery with Large Language Models
Shuyi Jia, Chao Zhang, Victor Fung
Discovering new materials can have significant scientific and technological implications but remains a challenging problem today due to the enormity of the chemical space. Recent a…