78 citations · 119 across the 5 of their papers we have counts for
5 papers
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…
PiNNwall: Heterogeneous Electrode Models from Integrating Machine Learning and Atomistic Simulation
Thomas Dufils, Lisanne Knijff, Yunqi Shao +1
Electrochemical energy storage always involves the capacitive process. The prevailing electrode model used in the molecular simulation of polarizable electrode-electrolyte systems…
Electric Double Layer at the Metal-Oxide/Electrolyte Interface
Lisanne Knijff, Mei Jia, Chao Zhang
Metal-oxide surfaces act as both Brønsted acids and bases, which allows the exchange of protons with the electrolyte solution and generates either positive or negative proton charg…
Machine Learning Inference of Molecular Dipole Moment in Liquid Water
Lisanne Knijff, Chao Zhang
Molecular dipole moment in liquid water is an intriguing property, partly due to the fact that there is no unique way to partition the total electron density into individual molecu…
PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials
Yunqi Shao, Matti Hellström, Pavlin D. Mitev +2
Atomic neural networks (ANNs) constitute a class of machine learning methods for predicting potential energy surfaces and physico-chemical properties of molecules and materials. De…