activity
20192026
most citedPiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials

78 citations · 259 across the 8 of their papers we have counts for

collaborators

9 papers

cond-mat.mtrl-sci2026

Stress-based structure optimization for a range-separated hybrid van der Waals density functional

Per Hyldgaard, Yunqi Shao, Raul Quintero-Monsebaiz +1

Complex matter, often partly soft and pliable, is the generic form of material systems. We must therefore generally call on density functional theory (DFT) to first predict the ato…

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-sci2023★ 22 cited

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…

physics.chem-ph2023★ 30 cited

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…

physics.chem-ph2022

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…

physics.chem-ph2021★ 15 cited

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…

cond-mat.soft2021★ 46 cited

Importance of the ion-pair lifetime in polymer electrolytes

Harish Gudla, Yunqi Shao, Supho Phunnarungsi +2

Ion-pairing is commonly considered as a culprit for the reduced ionic conductivity in polymer electrolyte systems. However, this simple thermodynamic picture should not be taken li…

physics.chem-ph2019★ 68 cited

Temperature effects on the ionic conductivity in concentrated alkaline electrolyte solutions

Yunqi Shao, Matti Hellström, Are Yllö +4

Alkaline electrolyte solutions are important components in rechargeable batteries and alkaline fuel cells. As the ionic conductivity is thought to be a limiting factor in the perfo…

physics.comp-ph2019★ 78 cited

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…