78 citations · 156 across the 8 of their papers we have counts for
8 papers
Molecular Modelling of Aqueous Batteries
Alicia van Hees, Zhan-Yun Zhang, Aishwarya Sudhama +1
Aqueous batteries play an increasingly important role for the development of sustainable and safety-prioritised energy storage solutions. Compared to conventional lithium-ion batte…
Salt effects on the mechanical properties of ionic conductive polymer: a molecular dynamics study
Harish Gudla, Kristina Edström, Chao Zhang
Functoinal polymers can be used as electrolyte and binder materials in solid-state batteries. This often requires performance targets in terms of both transport and mechanical prop…
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…
Bruce-Vincent transference numbers from molecular dynamics simulations
Yunqi Shao, Chao Zhang
Transference number is a key design parameter for electrolyte materials used in electrochemical energy storage systems. However, the determination of the true transference number f…
Transference number in polymer electrolytes: mind the reference-frame gap
Yunqi Shao, Harish Gudla, Daniel Brandell +1
The transport coefficients, in particular the transference number, of electrolyte solutions are important design parameters for electrochemical energy storage devices. Recent obser…
Training Algorithm Matters for the Performance of Neural Network Potential: A Case Study of Adam and the Kalman Filter Optimizers
Yunqi Shao, Florian M. Dietrich, Carl Nettelblad +1
One hidden yet important issue for developing neural network potentials (NNPs) is the choice of training algorithm. Here we compare the performance of two popular training algorith…
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…